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In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review. Cities with tie votes will be updated to have two winners!) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement series Here are the winners in the : Asia region Top Startup Cities in East Asia Seoul The Startup of the Year in is with 377 votes! Seoul dable Central is the Startup of the Year! It came out ahead with 121 votes. UpNow Central Sheung Wan came out as the Startup of the Year in with 154 votes. Votee Sheung Wan Tokyo The Startup of the Year in is with 41 votes! Tokyo ACES Wan Chai The Startup of the Year in is with 54 votes! Wan Chai Zetl Osaka, Japan In , is the winner! Osaka Nanophoton Taiwan came out as the Startup of the Year in ! Tico Taiwan Dongcheng, China In , is declared the Startup of the Year! Dongcheng Waiju Taipei came out as the Startup of the Year in ! Tico Taipei Akasaka In , is the winner! Akasaka WAKUWAKU Chuo In , is declared the Startup of the Year! Chuo GO-NET Ginza came out as the Startup of the Year in ! Netyear Ginza Kanazawa In , is the winner! Kanazawa Granzella Minami-aoyama In , is declared the Startup of the Year! Minami-aoyama Tokyo6 Entertainment Shibuya came out as the Startup of the Year in ! Hupro Japan Shibuya Toranomon In , is the winner! Toranomon Small Worlds Toshima In , is declared the Startup of the Year! Toshima Altplus Guangzhou, China came out as the Startup of the Year in ! Leyaoyao Guangzhou Hong Kong, Hong Kong In , is declared the Startup of the Year! Hong Kong Episode Six Kunming In , is the winner! Kunming Green A Sapporo In , is declared the Startup of the Year! Sapporo Polar Star Space Shirokanedai came out as the Startup of the Year in ! Nupp1 Shirokanedai Wuhan In , is the winner! Wuhan CSICEE Baoan, China In , is declared the Startup of the Year! Baoan Jumper Baoshan, China In , is declared the Startup of the Year! Baoshan Renren Games Chengdu, China came out as the Startup of the Year in ! Ju You Xi Chengdu Fujisawa, Japan In , is the winner! Fujisawa Scohia Pharma Fukuoka, Japan In , is declared the Startup of the Year! Fukuoka Kyulux Haidian, China came out as the Startup of the Year in ! 51vv Haidian Hongkou, China In , is the winner! Hongkou Kidstone Jiading, China In , is declared the Startup of the Year! Jiading IMRobotic Kawasaki, Japan came out as the Startup of the Year in ! QD Laser, Inc. Kawasaki Kyoto, Japan In , is declared the Startup of the Year! Kyoto Hacarus Minato, Japan In , is the winner! Minato Visionary Fun Nagoya, Japan came out as the Startup of the Year in ! Prodrone Nagoya Ningbo, China The winner in is . Ningbo OlymTech Corporation Aomori In , is declared the Startup of the Year! Aomori Forte Japan Bandai-cho In , is the winner. Bandai-cho Delta-Fly Pharm Changchun In , the Startup of the Year is . Changchun Kaidi Animation Fuchu In , is declared the Startup of the Year! Fuchu TMS Fukuoka-shi The winner in is . Fukuoka-shi SkyDisc Fukushima In , the Startup of the Year is . Fukushima Matsuri Technologies Gotanda In , is the winner. Gotanda Basset Hachioji In , is declared the Startup of the Year! Hachioji Adriakaim Harbin The winner in is J . Harbin oining Technology Iwaki In , is the winner. Iwaki Tao Engineering Izumo In , the Startup of the Year is . Izumo PuREC Kagoshima In , is declared the Startup of the Year! Kagoshima Farmers Support Kamakura In , the Startup of the Year is . Kamakura KAYAC Kami-meguro The winner in is . Kami-meguro Buzzreach Kanagawa In , is the winner. Kanagawa LoiLo Kashiwa In , the Startup of the Year is . Kashiwa Intelligent Surfaces Kita In , is declared the Startup of the Year! Kita Ax Robotix Kobe The winner in is . Kobe Coconomi Koriyama In , the Startup of the Year is . Koriyama Tocoton Kumamoto In , is the winner. Kumamoto Bloom Technology Kunitachi In , is declared the Startup of the Year! Kunitachi Trys Liaocheng The winner in is . Liaocheng Jinzheng Animation Minami In , is declared the Startup of the Year! Minami ATLAB Miyazaki In , is the winner. Miyazaki Caster Nagano In , is declared the Startup of the Year! Nagano Precision Forestry Nagaoka The winner in is . Nagaoka Plant Form Nihombashi-hamacho In , the Startup of the Year is . Nihombashi-hamacho Shizen Energy Niigata In , is declared the Startup of the Year! Niigata UniBio Japan Nishi-shinagawa In , is the winner. Nishi-shinagawa Play Heart Okayama In , is the winner! Okayama Arch Farm Okinawa In , is declared the Startup of the Year! Okinawa BlogNinja Otemachi In , the Startup of the Year is . Otemachi beBit, Inc Qingdao Shi In , is the winner! Qingdao Shi Manyoubang Quanzhou In , is declared the Startup of the Year! Quanzhou KungFu Animation Group Roppongi The winner in is . Roppongi Holo-X Saitama In , the Startup of the Year is . Saitama GameTector Sendai In , is the winner! Sendai Sendai Smart Machines Setagaya In , is declared the Startup of the Year! Setagaya Skyestate Shenzhen In , the Startup of the Year is . Shenzhen Aiator Shinjuku In , is declared the Startup of the Year! Shinjuku Scalar Shizuoka In , is the winner! Shizuoka PiPhotonics Sumida The winner in is . Sumida Holotch Tachikawa In , the Startup of the Year is . Tachikawa PlusOne Takamatsu In , is declared the Startup of the Year! Takamatsu Ken Robotec Tokushima In , is the winner! Tokushima DennoKotsu Toyonaka In , the Startup of the Year is . Toyonaka FISH BIOTECH Tsukuba In , is declared the Startup of the Year! Tsukuba WARPSPACE Wakayama In , the Startup of the Year is . Wakayama 4D Sensor Xiangzhou In , is the winner! Xiangzhou ALLiN Edutech Group Xianyang The winner in is . Xianyang TONLY Xiaoshan In , is declared the Startup of the Year! Xiaoshan GONGHUI Yamaguchi In , the Startup of the Year is . Yamaguchi Archis Yangpu The winner in is . Yangpu Tmxmall Yau Tsim Mong In , is the winner! Yau Tsim Mong Airnugget Yeoksamdong In , the Startup of the Year is . Yeoksamdong WizSchool Yichang In , is declared the Startup of the Year! Yichang Hubei Kento Electronic Yokohama In , is the winner! Yokohama Next Innovation Yubei In , the Startup of the Year is . Yubei Ophiux Network Yueqing The winner in is . Yueqing Yueqing Easythink Media Zhuhai In , is the winner! Zhuhai Aijuba Hongo In , the Startup of the Year is . Hongo Smile Robotics, Inc Admiralty In , is declared the Startup of the Year! Admiralty BUNCH Southern In , the Startup of the Year is . Southern Retykle Yongdungpo In , is the winner! Yongdungpo Push News Taichung In , is declared the Startup of the Year! Taichung Wondercise Cyberport, Hong Kong In , the Startup of the Year is . Cyberport Animoca Brands Hefei, China In , is the winner! Hefei Tsinglink New Territories, Hong Kong The winner in is . New Territories, Hong Kong Zeek Dezhou In , the Startup of the Year is . Dezhou Dezhou Baji Kitakyushu In , is declared the Startup of the Year! Kitakyushu Reif Seongnam In , is the winner! Seongnam EOFlow Yanshi In , the Startup of the Year is . Yanshi CASI Vision Top Startup Cities in South Asia Hyderabad, India The Startup of the Year in is with 962 votes Hyderabad Quixy Lahore, Pakistan is the Startup of the Year! It came out ahead with 186 votes. SmartWindows Lahore Bangalore, India When it comes to , is the Startup of the Year with 210 votes! Bangalore vBridge Hub Pune, India couldn’t be beaten for Startup of the Year in , it won with 129 votes! InfraCloud Pune Jaipur, India The Startup of the Year in is with 166 votes Jaipur Emizen Tech Lucknow, India is the Startup of the Year! It came out ahead with 279 votes. Keeros Superfoods Lucknow Noida, India came out as the Startup of the Year in with 156 votes! Bagisto Noida Mumbai, India When it comes to , is the Startup of the Year with 96 votes! Mumbai Sequretek Sylhet couldn’t be beaten for Startup of the Year in , it won with 122 votes! DataXpie Sylhet Mylapore, India In , is the winner! Mylapore Yalimart Goa, India In , the Startup of the Year is Goa Filter Coffee Delhi, India In , is declared the Startup of the Year! Delhi EkAnek Networks Karachi, Pakistan The winner in is Karachi Bykea Ahmedabad, India In , is the winner Ahmedabad MageComp Dhaka, Bangladesh The winner in is Dhaka SOLshare Islamabad, Pakistan In Islamabad, is declared the Startup of the Year! TAG Gurgaon, India In , the Startup of the Year is Gurgaon ixigo Bengaluru, India In , is the winner! Bengaluru Prakshep Private Limited Haryana, India couldn’t be beaten for Startup of the Year in ! Footprints Childcare Haryana Kolkata, India In , is the winner! Kolkata Wow! Momo New Delhi, India In , is declared the Startup of the Year! New Delhi Rusk Media Indore, India The winner in is Indore Chattar Patar Kormangala In , is the winner Kormangala Sanctum Technologies Ghaziabad came out as the Startup of the Year in Craftfurnish.com Ghaziabad! Mohali In , the Startup of the Year is Mohali DataKund Mysore The Startup of the Year in is Mysore Excelsoft Karnal came out as the Startup of the Year in ! Dunar Karnal Kolhapur In , is the winner! Kolhapur Zanvar Group Patiya In , the Startup of the Year is Patiya Kloud Project Kathmandu In , is declared the Startup of the Year! Kathmandu Vairav Technology Bangalore City, India In is the winner! Bangalore City, PagarBook Ernakulam The winner in is Ernakulam Epixel Solutions Colombo, Sri Lanka couldn’t be beaten for Startup in the Year in MAs Foods Colombo Andheri, India When it comes to , is the Startup of the Year! Andheri DoorMint Kanpur, India came out as the Startup of the Year in ! ServiceWalaa Kanpur Faisalabad In , is the winner! Faisalabad DigiKhata Nasik The Startup of the Year in is Nasik Sahyadri Farms Patna came out as the Startup of the Year in ! Asvin Tech Patna Chennai, India In , is the winner! Chennai Agnikul Cosmos Faridabad, India In , is declared the Startup of the Year! Faridabad UClean Panjim, India The winner in is Panjim TempoGO Ranchi, India In , is the winner Ranchi Osam Dairy Surat, India In , is declared the Startup of the Year! Surat Wishbook Thane, India In , is the winner! Thane Delight Foods Trivandrum, India came out as the Startup of the Year in ! GenRobotic Trivandrum Vijayawada, India In , the Startup of the Year is Vijayawada Eruvaka Technologies Visakhapatnam, India In , Ltd is declared the Startup of the Year! Visakhapatnam Greenline Labs Pvt Ahmadabad In , is the winner! Ahmadabad KiwiQA Services Bandra came out as the Startup of the Year in ! Car Club Bandra Bombay In , is the winner! Bombay Locobuddy Mobile Technology (Sportobuddy) Cochin In , the Startup of the Year is Cochin Tuttifrutti Interactive Gandhinagar In , is declared the Startup of the Year! Gandhinagar Kroop AI Guwahati In , the Startup of the Year is Guwahati GNRC Hospitals Jamshedpur In , is declared the Startup of the Year! Jamshedpur Intelliber Kabul The winner in is Kabul Miracle Shop (Vocinno Technology) Karur In , is the winner Karur Karur Vysya Bank Kochi The winner in is Kochi BestDoc Kozhikode In , is declared the Startup of the Year! Kozhikode OfficeKit HR Ludhiana In , the Startup of the Year is Ludhiana Trendy Beat Network Madhapur In , is the winner! Madhapur Head Digital Works Mangalore When it comes to , is the Startup of the Year Mangalore Hangyo Nagpur came out as the Startup of the Year in ! UPrudence Agro Nagpur Rawalpindi The Startup of the Year in is Rawalpindi Pro Options Marketing Rishikesh came out as the Startup of the Year in ! Descatuk Rishikesh Secunderabad couldn’t be beaten for Startup of the Year in Prakriti Biosystems Engineering Secunderabad Tirupur In , is the winner! Tirupur Tubeknit Fashions Udupi The winner in is Udupi Krishitantra Top Startup Cities in Southeast Asia Singapore, Singapore The Startup of the Year in is with 6556 votes! Singapore Cake DeFi Hanoi, Vietnam is the Startup of the Year! It came out ahead with 683 votes. DeFi For You Hanoi Kuala Lumpur, Malaysia came out as the Startup of the Year in with 109 votes! Trambellir Kuala Lumpur Pasig, Philippines When it comes to , . is the Startup of the Year with 110 votes! Pasig Quickreach Inc Bangkok, Thailand couldn’t be beaten for Startup in the Year in , it won with 60 votes! Perception Codes Bangkok Johor Bahru In , the Startup of the Year is . Johor Bahru AGR1 Bandung, Indonesia In , is declared the Startup of the Year! Bandung Prosa Portugal The winner in is . Portugal SCEMAI Tangerang, Indonesia In , is the winner Tangerang Yummy Corp Jakarta, Indonesia In , is the winner! Jakarta Tokocrypto Dki Jakarta, Indonesia In , is declared the Startup of the Year! Dki Jakarta Limakilo Bangkok Metropolis In , is the winner! Bangkok Meticuly Medan The winner in is ! Medan DIKA Manila, Philippines In , is declared the Startup of the Year! Manila PawnHero Phnom Penh, Cambodia In , the Startup of the Year is Phnom Penh vKirirom Bali In , is the winner! Bali Radius App Penang When it comes to , is the Startup of the Year Penang EasyParcel Surabaya came out as the Startup of the Year in ! Super Surabaya Setiabudi, Indonesia The Startup of the Year in is Setiabudi Chilibeli Cebu City came out as the Startup of the Year in MiCab Cebu City! Jawa couldn’t be beaten for Startup in the Year in DAUR Jawa Ho Chi Minh City, Vietnam In , is the winner! Ho Chi Minh City Pharmacity Selangor, Malaysia is the Startup of the Year! BabyDash Selangor Jakarta Pusat, Indonesia In , is the winner! Jakarta Pusat StickEarn Semarang When it comes to , is the Startup of the Year! Semarang Food Bear Petaling Jaya, Malaysia The winner in is . Petaling Jaya Deemples Kebayoran Baru, Indonesia couldn’t be beaten for Startup in the Year in ! Yogrt Kebayoran Baru Kuningan The winner in is . Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! Eden Shield Misgav Sharjah came out as the Startup of the Year in ! Bookends Sharjah Affula In , the Startup of the Year is . Affula eMotion Technologies Bene Beraq, Israel In , is the winner Bene Beraq Reflectiz Caesarea, Israel In , the Startup of the Year is . Caesarea CardiacSense Dubayy In , is declared the Startup of the Year! Dubayy North Ladder Muscat couldn’t be beaten for Startup in the Year in ! iLab Marine Muscat Tirat Carmel In , the Startup of the Year is . Tirat Carmel PassCall Advanced Technologies Hod Hasharon, Israel The winner in is . Hod Hasharon Idomoo Holon, Israel came out ahead as the Startup of the Year! OptimalPlus Holon Jeddah, Saudi Arabia When it comes to , is the Startup of the Year! Jeddah Arabian Combustion Technologies Ramat Hasharon, Israel is the Startup of the Year! Lumen Ramat Hasharon Al Khobar came out as the Startup of the Year in ! Swyft Al Khobar Bet Shemesh In , is the winner! Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Asia

What Capitalists Got Wrong About the 'Future of Education,' And What They Should Have Done Instead

Τι έκαναν λάθος οι καπιταλιστές σχετικά με το «μέλλον της εκπαίδευσης» και τι έπρεπε να κάνουν αντ 'αυτού

Lo que los capitalistas se equivocaron sobre el «futuro de la educación» y lo que deberían haber hecho en su lugar

Što su kapitalisti pogriješili u vezi s 'budućnošću obrazovanja' i što su trebali učiniti umjesto toga

Ki sa kapitalis yo te fè mal sou 'lavni edikasyon an,' ak sa yo ta dwe fè pito

資本家が「教育の未来」について何を間違えたのか、そして代わりに何をすべきだったのか

«Білім берудің болашағы» туралы капиталистер не қателесті және оның орнына не істеу керек еді

Ką kapitalistai suklydo dėl „švietimo ateities“ ir ką jie turėjo padaryti vietoj to

Капиталистууд "боловсролын ирээдүй"-ийн талаар юу буруу хийсэн бэ, үүний оронд юу хийх ёстой байсан бэ?

Imakunatan Kapitalistakuna pantarqanku ‘Educacionpa hamuq pachanmanta’, hinallataq imakunatan ruwananku karqan chaypa rantinpi

Ce au greșit capitaliștii cu privire la „viitorul educației” și ce ar fi trebuit să facă ei în schimb

ርእሰማላውያን ብዛዕባ ‘መጻኢ ትምህርቲ’ እንታይ ተጋግዮም፡ ኣብ ክንድኡ ድማ እንታይ ክገብሩ ነይርዎም

Kapitalistlar "Ta'lim kelajagi" haqida nima xato qilishdi va ular o'rniga nima qilishlari kerak edi

Nature + Biomimicry + Bitcoin: Mutually Beneficial Partnership

Naturaleza + Biomimetismo + Bitcoin: una alianza mutuamente beneficiosa

Luonto + Biomimikri + Bitcoin: molemminpuolisesti hyödyllinen kumppanuus

प्रकृति + बायोमिमिक्री + बिटकॉइन: पारस्परिक रूप से लाभकारी साझेदारी

Nature + Biomimicry + Bitcoin. փոխշահավետ գործընկերություն

自然 + バイオミミクリー + ビットコイン: 相互に利益のあるパートナーシップ

Nature + Biomimicry + Bitcoin: ორმხრივად მომგებიანი პარტნიორობა

ធម្មជាតិ + Biomimicry + Bitcoin៖ ភាពជាដៃគូដែលមានអត្ថប្រយោជន៍ទៅវិញទៅមក

Natiora + Biomimicry + Bitcoin: Fiaraha-miombon'antoka

Masikirwo + Biomimicry + Bitcoin: Kudyidzana Kunobatsira Kudyidzana

Природа + биомимикрија + биткоин: обострано корисно партнерство

இயற்கை + பயோமிமிக்ரி + பிட்காயின்: பரஸ்பர நன்மை பயக்கும் கூட்டாண்மை

Tabiat + Biomimicry + Bitcoin: O'zaro manfaatli hamkorlik

Is Artificial Intelligence in Healthcare a Ticking Time Bomb or Timely Solution?

Science: The Way of Nature

ሳይንስ፡ የተፈጥሮ መንገድ

Ciencia: Naturaleza ukan thakhipa

Наука: Пътят на природата

বিজ্ঞান: প্রকৃতির পথ

Ciencia: El camino de la naturaleza

Zientzia: Naturaren Bidea

מדע: דרך הטבע

科学：自然の道

ວິທະຍາສາດ: ວິທີການຂອງທໍາມະຊາດ

Nauka: Droga Natury

ساینس: د طبیعت لاره

Наука: Пут природе

Solving the Paradox of Decentralized Coordination

Metamorphosis Commenced, vol2: Crecimiento Aleph Review & Q4 in Lisbon and Thailand

Metamorphosis Commended, vol2: Crecimiento Aleph Review & Q4 v Lisabonu a Thajsku

Metamorphosis Commenced, τόμος 2: Crecimiento Aleph Review & Q4 στη Λισαβόνα και την Ταϊλάνδη

Metamorfosis iniciada, vol. 2: Revisión y cuarto trimestre de Crecimiento Aleph en Lisboa y Tailandia

Metamorphosis Commenced, vol2: Crecimiento Aleph Review & Q4 en Lisboa e Tailandia

変身開始、第2巻：クレシミエント・アレフのレビューとリスボンとタイでの第4四半期

Метаморфоз басталды, 2-том: Crecimiento Aleph шолуы және Лиссабондағы және Таиландтағы 4-тоқсан

Metamorphosis ເລີ່ມຕົ້ນ, vol2: Crecimiento Aleph Review & Q4 ໃນ Lisbon ແລະປະເທດໄທ

میټامورفوسس پیل شوی، vol2: Crecimiento Aleph Review & Q4 په لیزبون او تایلینډ کې

Metamorfosis qallarisqa, vol2: Crecimiento Aleph Revisión & Q4 Lisboapi, Tailandiapipas

Metamorphosis Yakatangwa, vol2: Crecimiento Aleph Ongororo & Q4 muLisbon neThailand

Metamorfoza filloi, vëll 2: Crecimiento Aleph Review & Q4 në Lisbonë dhe Tajlandë

Metamorphosis bắt đầu, tập 2: Đánh giá Crecimiento Aleph & Q4 tại Lisbon và Thái Lan

Decentralized Micro-Governance Model for the Kingdom of Tonga Based on Proof-of-Work Consensus

Top 10 Greatest Games of All Time 
According to the Hackernoon Team

Top 10 najboljih igara svih vremena 
Prema Hackernoon timu

Els 10 millors jocs de tots els temps 
Segons l'equip Hackernoon

Top 10 nejlepších her všech dob 
Podle Hackernoon Teamu

Die 10 besten Spiele aller Zeiten
Laut dem Hackernoon-Team

Los 10 mejores juegos de todos los tiempos
según el equipo de Hackernoon

हैकरनून टीम के अनुसार अब तक के 10 सबसे महान खेल

史上最高のゲームトップ10 
Hackernoonチームによる

Бардык убакыттардын эң мыкты 10 оюндары 
Hackernoon командасынын айтымында

د هر وخت غوره 10 غوره لوبې 
د هیکرنون ټیم په وینا

Топ 10 најбољих игара свих времена 
Према Хацкерноон тиму

Top 10 Бузургтарин бозиҳои ҳама вақт 
Бино ба маълумоти Hackernoon Team

Top 10 Greatest Games of All Time 
Ngokutsho kweQela leHackernoon

Most people don’t know that behind huge renewable energy projects lies a hidden cost—e-waste from solar panels. This is becoming a significant problem in places like India, where solar energy investment is booming. With a lifespan that lasts between 25 and 30 years, we are now discovering that this energy alternative has become one of the largest contributors to modern e-waste. This isn’t expected to slow down as countries worldwide continue to announce new renewable energy projects in the years to come. For example, Waree Energies, an India-based solar panel company, recently announced it would establish its first U.S. solar module manufacturing facility in Texas, which is looking to open its door by 2025. The rush to build large-scale projects often prioritizes growth over sustainability. However, there’s a better approach: empowering communities and individuals through smaller-scale solar solutions. Let’s examine how solar panels contribute to major e-waste and how the sector can channel solutions to this issue. Big e-waste contributors Solar panels are commonly referred to as a green technology, yet their environmental footprint extends beyond their operational phase. Past its lifecycle, this technology pollutes before going to market with a wasteful production process and a polluting afterlife. The production of solar panels involves extracting and processing raw materials such as silicon, silver, and various rare earth elements—a process that is energy-intensive and generates significant emissions. In fact, experts say the waste will be more costly than the impact on electricity production. The end-of-life stage of solar panels also presents a significant challenge. While the lifespan of a solar panel is typically 25-30 years, disposal and recycling infrastructure is not adequately developed, yet it requires very meticulous processes to avoid causing environmental and health risks. Many panels end up in landfills, racking up e-waste. According to the International Renewable Energy Agency (IRENA), planet Earth could see larger amounts of annual solar panel waste by the early 2030s, projecting a total of 28 million tonnes by 2025. Unfortunately, reusing solar panels hasn’t provided much of a solution to the growing problem, including for residential purposes. Used panels, which may have reduced efficiency, don’t offer significant savings. With installation costs and other equipment making up about half the cost of a residential solar array, the marginal savings on used panels do not justify their lower output. Thankfully, modern solar panels have become more affordable and efficient, providing an incentive for residential properties to replace existing ones and for other homeowners to join the solar energy revolution. As a temporary solution, many expired solar panels are being sent to developing countries to power water pumps for farming in low-income communities. Unfortunately, it doesn’t mitigate the overall environmental impact it poses in the near future. Investing in Smaller-Scale Solutions Investing in smaller-scale and community-focused solutions is a promising strategy to mitigate the e-waste issue while still benefiting from solar technology. In the first half of 2023, $358 billion was invested in the renewable energy sector, with $335 billion directed towards project deployment through asset finance and small-scale solar initiatives. This approach emphasizes producing just the right amount of energy needed for specific applications, reducing the excess and waste associated with large-scale installations. In recent years, supporting small-scale initiatives has proven to be a valuable approach to tackling the issue. A recent study comparing large and small solar PV systems confirmed that small-scale solar solutions are less environmentally harmful than large-scale solar systems. Additionally, smaller solar panels can effectively meet the needs of communities with limited access to reliable power sources. These panels can provide essential services like lighting, which is crucial for safety and running daily activities. For example, our team at Techko Group witnessed first-hand the impact a small-scale solar solution can have on a community—the Taiwanese Atayal Tribe. During a heavy storm, our solar-powered lights enabled their local school to continue its Halloween costume parade despite power outages, showcasing localized solar projects’ immediate and practical benefits. Prioritizing small-scale, community-driven projects can meet local energy needs without the downsides of large-scale installations. An example of successful implementation can be seen in various regions like Africa, where off-grid solar solutions have provided reliable electricity to remote villages, enhancing quality of life and economic opportunities without contributing significantly to e-waste. While intentional practices in clean tech production are required, including government regulations and efficient recycling programs, investing in smaller communities is another step in fostering sustainable development. Localized solar projects reduce the technology’s environmental impact by cutting down on transportation emissions, avoiding massive waste, and providing tailored energy solutions that match the community’s needs. For renewable energy solutions like solar, it’s all about starting small before going big. Ted Ko, President of The Techko Group Most people don’t know that behind huge renewable energy projects lies a hidden cost—e-waste from solar panels. This is becoming a significant problem in places like India , where solar energy investment is booming. With a lifespan that lasts between 25 and 30 years, we are now discovering that this energy alternative has become one of the largest contributors to modern e-waste. significant problem in places like India This isn’t expected to slow down as countries worldwide continue to announce new renewable energy projects in the years to come. For example, Waree Energies, an India-based solar panel company, recently announced it would establish its first U.S. solar module manufacturing facility in Texas, which is looking to open its door by 2025. recently announced The rush to build large-scale projects often prioritizes growth over sustainability. However, there’s a better approach: empowering communities and individuals through smaller-scale solar solutions. Let’s examine how solar panels contribute to major e-waste and how the sector can channel solutions to this issue. Big e-waste contributors Solar panels are commonly referred to as a green technology, yet their environmental footprint extends beyond their operational phase. Past its lifecycle, this technology pollutes before going to market with a wasteful production process and a polluting afterlife. The production of solar panels involves extracting and processing raw materials such as silicon, silver, and various rare earth elements—a process that is energy-intensive and generates significant emissions. In fact, experts say the waste will be more costly than the impact on electricity production. more costly The end-of-life stage of solar panels also presents a significant challenge. While the lifespan of a solar panel is typically 25-30 years, disposal and recycling infrastructure is not adequately developed, yet it requires very meticulous processes to avoid causing environmental and health risks. Many panels end up in landfills, racking up e-waste. According to the International Renewable Energy Agency (IRENA), planet Earth could see larger amounts of annual solar panel waste by the early 2030s, projecting a total of 28 million tonnes by 2025. 28 million tonnes Unfortunately, reusing solar panels hasn’t provided much of a solution to the growing problem, including for residential purposes. Used panels, which may have reduced efficiency, don’t offer significant savings. With installation costs and other equipment making up about half the cost of a residential solar array, the marginal savings on used panels do not justify their lower output. Thankfully, modern solar panels have become more affordable and efficient, providing an incentive for residential properties to replace existing ones and for other homeowners to join the solar energy revolution. As a temporary solution, many expired solar panels are being sent to developing countries to power water pumps for farming in low-income communities. Unfortunately, it doesn’t mitigate the overall environmental impact it poses in the near future. Investing in Smaller-Scale Solutions Investing in smaller-scale and community-focused solutions is a promising strategy to mitigate the e-waste issue while still benefiting from solar technology. In the first half of 2023, $358 billion was invested in the renewable energy sector, with $335 billion directed towards project deployment through asset finance and small-scale solar initiatives. This approach emphasizes producing just the right amount of energy needed for specific applications, reducing the excess and waste associated with large-scale installations. $358 billion was invested In recent years, supporting small-scale initiatives has proven to be a valuable approach to tackling the issue. A recent study comparing large and small solar PV systems confirmed that small-scale solar solutions are less environmentally harmful than large-scale solar systems. recent study Additionally, smaller solar panels can effectively meet the needs of communities with limited access to reliable power sources. These panels can provide essential services like lighting, which is crucial for safety and running daily activities. For example, our team at Techko Group witnessed first-hand the impact a small-scale solar solution can have on a community—the Taiwanese Atayal Tribe. During a heavy storm, our solar-powered lights enabled their local school to continue its Halloween costume parade despite power outages, showcasing localized solar projects’ immediate and practical benefits. Prioritizing small-scale, community-driven projects can meet local energy needs without the downsides of large-scale installations. An example of successful implementation can be seen in various regions like Africa, where off-grid solar solutions have provided reliable electricity to remote villages, enhancing quality of life and economic opportunities without contributing significantly to e-waste. While intentional practices in clean tech production are required, including government regulations and efficient recycling programs, investing in smaller communities is another step in fostering sustainable development. Localized solar projects reduce the technology’s environmental impact by cutting down on transportation emissions, avoiding massive waste, and providing tailored energy solutions that match the community’s needs. For renewable energy solutions like solar, it’s all about starting small before going big. Ted Ko, President of The Techko Group Ted Ko, President of The Techko Group Ted Ko, President of The Techko Group Ted Ko , President of The Techko Group Ted Ko Techko Group

How Small-Scale Solar Systems Can Mitigate E-Waste and Empower Communities

First, the moral reason. Despite India being among the fastest growing economies of the world, and the world singing hosannas to the emerging Indian middle class, it scores extremely poorly on most human development indices. The fact that India now has a booming automobile industry with all manner of cars and SUVs choking the sidewalks of our urban centers cannot mask the co-existing abysmal poverty, that too is all too evident. Poverty is not an invisible underbelly in India. It is upfront and stark, but we all pretend that we don't see it. Much as the British colonialists went to their clubs, gymkhanas, and golf courses in pre-independence India, not caring to notice the plight of the common Indian who lived an emaciated existence, much of middle-class India, which is the backbone of the new corporate sector, is blind to the misery of their fellow Indians. This does not reflect well upon us as a people. Where is our moral conscience? How can we be so insensitive to our fellow human beings? The second and perhaps decisive reason is self-interest. The present economic development model is not tenable. It will lead to tremendous unrest. You have to make everybody join in the good times. Look at the sheer numbers of the deprived. How long will they silently watch as the privileged amongst us live better and bigger than ever before? Now more than ever before, we need everybody in the fold. The benefits that will accrue will be tremendous. As we help marginalized people join the mainstream, they will eventually become economic force multipliers. They will lead to economic activity of a scale that will be global in its ramifications. The government is doing what it can to help. The private sector has to pitch in big time- in our cities, towns, villages, and tribal areas. It has to build schools, plant trees, help with rainwater harvesting, clothe and feed the poor, and distribute books. There really is so much that can be done in India. It is heartening to see that companies in the corporate sector are now joining hands with the government and NGOs and helping with community development and environmental issues, besides pure philanthropy. This needs to continue and be taken to the next level. We need our Andrew Carnegies, Warren Buffets, Bill Gates, and Rockefellers. The smaller companies have to get their act together so that CSR is given the prime importance that is due to it. The media professionals have to pitch in by providing adequate coverage to all manner of CSR activity. PR and advertising plans should always have a CSR component, as the easiest thing to do in India is some form of social activity.  There is so much that is wrong and needs to be righted. From uncovered drains to dirty toilets, from streets without lighting to classrooms without books- there are so many causes that can be taken up. Let CSR be the number one priority for the next decade, and see how India moves up the human development indices. Only then will we be able to hold our own on the world stage. All the pretty people in the world driving around in their Ferraris won't do it for us. It is our masses who will do it for us. But they have to be clothed and fed first. First, the moral reason. Despite India being among the fastest growing economies of the world, and the world singing hosannas to the emerging Indian middle class, it scores extremely poorly on most human development indices. The fact that India now has a booming automobile industry with all manner of cars and SUVs choking the sidewalks of our urban centers cannot mask the co-existing abysmal poverty, that too is all too evident. Poverty is not an invisible underbelly in India. It is upfront and stark, but we all pretend that we don't see it. Much as the British colonialists went to their clubs, gymkhanas, and golf courses in pre-independence India, not caring to notice the plight of the common Indian who lived an emaciated existence, much of middle-class India, which is the backbone of the new corporate sector, is blind to the misery of their fellow Indians. This does not reflect well upon us as a people. gymkhanas, Where is our moral conscience? How can we be so insensitive to our fellow human beings? The second and perhaps decisive reason is self-interest. The present economic development model is not tenable. It will lead to tremendous unrest. You have to make everybody join in the good times. Look at the sheer numbers of the deprived. How long will they silently watch as the privileged amongst us live better and bigger than ever before? Now more than ever before, we need everybody in the fold. The benefits that will accrue will be tremendous. As we help marginalized people join the mainstream, they will eventually become economic force multipliers. They will lead to economic activity of a scale that will be global in its ramifications. The government is doing what it can to help. The private sector has to pitch in big time- in our cities, towns, villages, and tribal areas. It has to build schools, plant trees, help with rainwater harvesting, clothe and feed the poor, and distribute books. There really is so much that can be done in India. It is heartening to see that companies in the corporate sector are now joining hands with the government and NGOs and helping with community development and environmental issues, besides pure philanthropy. This needs to continue and be taken to the next level. We need our Andrew Carnegies, Warren Buffets, Bill Gates, and Rockefellers. The smaller companies have to get their act together so that CSR is given the prime importance that is due to it. The media professionals have to pitch in by providing adequate coverage to all manner of CSR activity. PR and advertising plans should always have a CSR component, as the easiest thing to do in India is some form of social activity.  There is so much that is wrong and needs to be righted. From uncovered drains to dirty toilets, from streets without lighting to classrooms without books- there are so many causes that can be taken up. Let CSR be the number one priority for the next decade, and see how India moves up the human development indices. Only then will we be able to hold our own on the world stage. All the pretty people in the world driving around in their Ferraris won't do it for us. It is our masses who will do it for us. But they have to be clothed and fed first.

CSR Is a Must for Indian Companies Irrespective of Size

Artisanal Power: How to Avert a Threatened Mass Casualty Event

কারিগর শক্তি: কিভাবে একটি হুমকিমূলক গণহত্যার ঘটনা এড়ানো যায়

Handwerkliche Macht: Wie sich ein drohendes Massenopferereignis abwenden lässt

Energía artesanal: cómo evitar una amenaza de incidente con víctimas en masa

Pouvoir artisanal : comment éviter une menace d’événement causant des pertes massives

कारीगर शक्ति: सामूहिक दुर्घटना की आशंका को कैसे टालें

職人の力：脅威となる大量死傷事件を回避する方法

장인의 힘: 대규모 사상자 위협을 방지하는 방법

Poder artesanal: como evitar um evento com ameaça de vítimas em massa

Ремесленная сила: как предотвратить угрозу массового поражения

Zanaat Gücü: Tehdit Altındaki Kitlesel Kayıp Olayı Nasıl Önlenir?

Sức mạnh thủ công: Làm thế nào để ngăn chặn một sự kiện thương vong hàng loạt bị đe dọa

手工力量：如何避免大规模伤亡事件的威胁

Humans! One interesting twinkle in the dark, vacuous expanse of the cosmos. Trapped on a floating ball of rock to mope into space and be tormented by relentless, conflicting emotions. Prison within and prison without. Awaiting the summon of death. What the hell is this existence and what is its use? At least we have each other. Right? Yes, we do. Plus, the resources of the earth. And most importantly, there's that small voice that keeps telling us to become more. So, we stagger on. Nothing is certain in this great maze of existence but we don't just give in. We grope for purpose in the confusing unknown. We do what we do in search of fulfillment. Thus, we solve problems for those we love. We really dig in and harvest ingenious creations from within us. It makes us happy, so we keep going. We keep chasing that which our heart yearns for. What is it many have asked? What is it that we really want? Some of the smartest among us have given a name to it, and I dare agree with them. It is what it is; Freedom! Freedom from wants, death, and ourselves. Freedom from whatever dares limit us. Freedom from this great bondage that is us. Hence, in our relentless yearning, we build this great bridge of science. Into matter, into space, into life. Into technology! Oh that we may find that which we seek. Oh that we may find; Autonomy! Autonomy Has Always Driven Technology Humans have always been bound by limitations that we constantly rebel against. Imagine the caveman days. Small, slow, and weak as we were, we were completely bound by the harsh laws of the jungle. Dominated by powerful creatures. Yet, our big brain brought us to the very top of the food chain. Inventions like the spear, knife, and arrow with advanced hunting strategies helped us overcome the powerful beasts that ruled the wild. Then, we created our own societies and were ruled by tribal laws. Not very different jungle laws though but strong humans were in charge now. Then came the reign of religion, and priests were in charge. Then came countries with cunning politicians. Then came capitalism with wealthy businessmen who are now running the show. All these changes have been driven by advancements as humans try to take more control of their lives. From being ruled by wild beasts to strong men to priests to politicians and businessmen, people have always invented systems that gave them more control of their lives. And this progress is not only evident in the technology of our leadership but also in the everyday devices that we use. First, we had caves and stone tools, then came villages and metal tools, and then came towns and analog machines. Now, we have cities and digital technology. This drive for autonomy in our technology has culminated in what we call a decentralized internet or web3. Autonomy in Web3 Web 3 remains a relatively recent technology that is yet to be widely understood. Yet, it is already being widely adopted as the advantages are very obvious. Simply put, web3 aims to put the Internet and financial system under the control of everyone and not just a few giant corporations. Why is this necessary you may ask? Well, the answer is simple: "Power corrupts and absolute power corrupts absolutely!" The internet and financial systems represent the most critical technologies that humans have been able to develop. Hence, there is a great power in the hands of anyone who controls them. It's no wonder that top government officials and tech billionaires dominate the list of most powerful people in the world. This great power which allows such people to exercise control around the world is akin to the legendary absolute power that absolutely corrupts the wielders. And we have already seen this corruption at play. For example; , , , etc. Facebook has recorded multiple user data breaches Twitter has been accused many times of stifling free speech US politicians were caught cheating through insider trading in the stock market Hence, the internet and financial market which have lasting effects on people’s lives need some level of democracy. This is what web3 promises. Cryptocurrencies were introduced as a means for people to transact directly without passing through middlemen in the banking institutions who often benefit fraudulently from people’s money with little or no punishment. Combining crypto with blockchain technology provides the world with a global decentralized financial internet that no one fully controls and everyone can benefit from. Is Autonomy Truly Possible? Many reservations have been raised about the practicality of web3 and whether it can be truly autonomous. For example; fundamental blockchain infrastructures are often hosted on web2 servers with centralized control. Also, mining cryptocurrency is energy intensive which makes the entire system dependent on power companies. Would the power companies and server owners not be the new bosses in town? Well, that's a critical problem that is yet to be solved. However, blockchain technology is slowly solving the centralized server problem through compressed distributed storage of the technology on the devices of its users. Basically, a blockchain is a database, and every device that connects to the blockchain has a version of the database stored on it. This makes it possible for the billions of devices that users use to connect to the blockchain to function as servers distributed around the world instead of just one central server. Fingers crossed for what will come in the future. About the electricity problem, well, most cryptocurrencies have a limited amount of tokens to be mined, hence, mining is not a forever thing. Also, alternative versions of power like solar make it possible for miners to set up affordable sources of personalized power. Hence, centralized power companies can possibly be bypassed. With these considerations, the autonomy that web3 promises can foreseeably be accomplished. Future of Autonomy Picture this... You fall asleep now and awaken in the autonomous age. Robots run the world now. The planet is one big country run by the United Nations and headquartered in Earth City (a giant circular artificial island built in the Atlantic Ocean on longitude 0 degrees and latitude 0 degrees). Networks of underwater hyperloop lines and speed train bridges interconnect all the continents in minutes and the former countries are nothing but political provinces for proper documentation and security. Autonomous vehicles with little or no accidents dominate the roads and autonomous spaceships transverse space to transport people and resources around the solar system. Humans have finally colonized the solar system. The world lives in abundance as automated high-tech farms meet all our agricultural needs. Basic amenities like water, electricity, and education are free, and everyone gets a living wage to cover secondary needs like real estate around the planets, internet access, and transportation. Advanced amenities like technology, art, entertainment, exploration, and financial trading are where businesses operate. Web3 is fully operational. Hologram devices are all over the place for people to experience the dinterverse (decentralized internet metaverse) which uses all those devices as servers. DAOs, running on open-source technology, are the global means of voting, with Auth ID being the prominent open-source platform for decentralized technology. Industry-based crypto coins are the means of exchange (e.g., techon for technology, agrocoin for agriculture, muse for music, etc.) and cryptex trading (forex trading of the time) is the primary means of employment for everyone with people speculating on which industry will do better based on the season. Bitcoin is, however, immortalized as the only currency accepted around the world across all industries. Global poverty has been eradicated and financial inequality is very low around the world with cryptex trading available to everyone. A decentralized financial system was the game changer as it led to an explosion of prosperity through relatively easy cryptex trading. You investigate your trade on your holophone. You're doing very well and will be making another $50,000 this week. You smile to yourself as you glance out the window of your noiseless starjet (silent speed jets that ferry people around the world in minutes). The city below is a great wonder as people said. You're on vacation in Earth City. It's your first time. You came for it. The statue of autonomy or "the human spirit" as it is fondly called. Many weep upon first beholding its glory. The symbol of human victory on the earth. It appears on the horizon. The shiny bust of a crowned androgynous human child stops at the waist so it's neither male nor female. Completely transparent as if entirely made of glass, hence, the name human spirit. With both hands upraised in freedom, it holds Bitcoin (the first global crypto) in the right hand and a scroll in the left. Your ship does the ceremonial circling of the statue. Then it passes the scroll and you behold those legendary words. You read it in a low voice. "We humans hold that all of us are born free. Free to live, free to love, free to prosper!" A smile crawls up your face, and you resolve to uphold this great motto. Then you hear a phone ring. A familiar ancient music. It gets louder and louder. It's your phone. You were dreaming of course. You curse as you pick up your phone. It's your boss calling you to return to work. You cut the call and sigh in disappointment. That dream was just beautiful. Then it occurs to you: The current world is one big nightmare! WHAT DOES THIS MEAN Web3 is upon us. Cryptocurrencies are everywhere and blockchain infrastructure gets more sophisticated every day. Many crypto millionaires roam the earth to prove the real value of the technology. Decentralization is on its way. Below are the predictions you should know about web3; Decentralization is the future. Digital commodities e.g., NFTs, in-game items, virtual real estate, and decentralized exchange (DEX) will create a new industry. Currencies will become more globalized. Companies will become smaller and more personalized. Self-service will become the new way of operating the financial industry. HOW DO I PREPARE FOR AUTONOMY Educate yourself on cryptocurrencies, blockchain technology, and the metaverse. Invest in promising crypto coins. Participate in public web3 forums. Fund web 3 projects if you can. Learn how to make money on your own from the internet.

Behold: The Autonomy We Always Sought

দেখুন: যে স্বায়ত্তশাসন আমরা সবসময় চেয়েছি

Siehe: Die Autonomie, die wir immer gesucht haben

He aquí: la autonomía que siempre buscamos

Voici : l’autonomie que nous avons toujours recherchée

देखिए: वह स्वायत्तता जो हम हमेशा से चाहते थे

見よ、私たちが常に求めていた自律性

보라: 우리가 항상 추구했던 자율성

Eis: a autonomia que sempre buscamos

Вот: автономия, к которой мы всегда стремились

Bakın: Her Zaman Aradığımız Özerklik

Kìa: Quyền tự chủ mà chúng tôi luôn tìm kiếm

看哪：我们一直寻求的自主权

That the world needs to transition to a green economy and follow an alternate vision of growth and development that is both self-sustainable and benign to the environment is not in doubt at all. What needs to be, however, not forgotten is the fact that such a seminal change in the growth model followed by the world cannot come about by good intentions alone. Merely expounding the virtues of wind, solar, geothermal, and other forms of sustainable and non-polluting energy sources without understanding and accounting for the immense challenges that come in the way of implementing such a paradigm change in energy generation methodologies will inevitably lead to failure. Like it or not, fossil fuels are here to stay for a long time. Till the time sufficient technological progress is made in ensuring alternative forms of energy generation are cheaper and more efficiently and conveniently available than fossil fuels, we will continue to use them. That is likely to be the likely scenario, at least for the foreseeable future. There really is no perfect substitute for fossil fuels in terms of wide-scale availability and ever-improving technology for extracting or producing these. It is important to remember that the advent of the fossil fuel age brought about tremendous improvements in the lives of people around the world, setting them on the course of unimaginable progress. Life in the pre-fossil fuel age wasn’t as rosy or utopian as one might imagine. People would clear away vast tracts of forests to use as firewood and building materials. Animal-drawn carts and carriages led to cities, towns, and villages becoming exceedingly dirty and diseases-prone on account of vast amounts of horse and cattle dung lying about on the streets. The modern global economy has come about because of the industrial revolution, which would not have been possible without fossil fuels. That has immense significance in the history of mankind, given that the industrial revolution allowed millions of people to emerge from poverty. It, in effect, made the modern world we see around us. That was how things were, but the world is at a tipping point with regard to the climate crisis brought about by global warming that has been, to a very large extent, caused by the humongous fossil fuel-powered global economy. [1] Challenges faced in transitioning to a green economy There are numerous challenges faced in transitioning to a green economy, the least of which is the high upfront cost accrued because of the adoption of green technology in terms of creating new infrastructure, retrofitting existing infrastructure and facilities, and training people in the use of the new technology. Besides, implementing green technology solutions is a long-term process that involves massive amounts of investment with no prospects of profitable returns for a very long time. Also, there is a limit to how much ground can be used as an alternative source of energy, like wind and solar cover, when it comes to completely replacing conventional sources of energy. If alternative green sources of energy cannot account for more than a quarter of the final energy consumption demand, can the world afford to move to these technologies at any time in the distant future? [2] It is all very well for nations around the world to set up green energy transition goals, but any forced or coercive measures to bring around this change may prove counterproductive. This is something that developing countries like India, which have a long way to go before they can be considered fully developed, have to be cognizant of when planning their growth strategies for the coming decades. Instead of using a green technology or nothing approach, what needs to be done to achieve global sustainability goals is to look at technology-agnostic policies that make it possible for all kinds of clean energy technologies to be given an equal opportunity to prove their worth. Advances in clean fossil technologies, for instance, may show the way for developing countries like India, where the demand for energy is expected to grow enormously as their economies grow in the years ahead. Natural gas, primarily comprising methane, for instance, emits half as much carbon dioxide when burnt than coal. What can also be done to clean up the environmental pollution caused by the use of fossil fuels is to capture the carbon dioxide generated and store it underground using the technique of carbon capture and sequestration. One can also try to enhance the efficiency of fossil fuel-run power plants. This will lead to a lower consumption of fuel and a lower amount of carbon dioxide emission. [3] What ultimately needs to be done is to find an integrated system of energy production and consumption that leaves a minimal impact on the environment, allowing it to repair itself and recover. Advocating romantic notions of living entirely off sunlight, air, and naturally occurring geothermal energy may prevent us from looking at the issue objectively and coming up with an environment-friendly growth and development policy that will actually work. https://www.brookings.edu/articles/why-are-fossil-fuels-so-hard-to-quit/#:~:text=We%20haven't%20found%20a,largely%20true%20for%20natural%20gas. [1] https://energypost.eu/the-dangers-of-green-technology-forcing/ [2] https://news.stanford.edu/2017/10/05/future-energy-fossil-fuels/ [3]

The Path to a Green Economy Cannot Wash Away Fossil Fuels for the Foreseeable Future

This paper is under CC 4.0 license. available on arxiv Authors: (1) Jingjing Wang, School of Computing; (2) Joshua Luo, The Westminster Schools; (3) Grace Yang, South Windsor High School; (4) Allen Hong, D.W. Daniel High School; (5) Feng Luo, School of Computing. Table of Links Abstract & Introduction Related Work Methodology Experiment Results Discussion References Abstract This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of spillovers on a single network. We provide an econometric framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global treatment effect. We show that the optimal clustering can be approximated as the solution of a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes easy-to-check conditions to choose between a cluster or individual-level randomization. We illustrate the method’s properties using unique network data from the universe of Facebook’s users and existing network data from a field experiment. : Experimental Design, Spillover Effects, Causal Inference, Cluster Designs. JEL Codes: C10, C14, C31, C54 Keywords 1 Introduction Consider a (large) population of n individuals connected under a single observed network. Researchers are interested in conducting an experiment to estimate the global average treatment effect, i.e., the difference between the average effect of treating all versus none of the individuals in the population. Treating an individual may generate spillovers to her friends in the network. To capture such effects, researchers conduct a cluster experiment. Individuals are first partitioned into clusters. Within a cluster, either all units are assigned to the treatment or all units are assigned to the control group. Finally, researchers estimate treatment effects by taking a difference between the average outcomes of treated and control units (possibly adjusting for baseline covariates). The cluster design does not require modeling the dependence of individual outcomes on neighbors’ assignments, but it requires a choice of clusters and some assumptions on the extend of the spillovers along the network. For example, cluster experiments on online platforms require choosing a partition of the social network, and field experiments require choosing the unit of randomization, such as villages or regions. This raises the question of how many and which clusters to use in experiments. Typical approaches in economic research assume prior knowledge of many independent clusters. There are many settings where this information is not available, and instead units in the population have different degrees of connections.[1] This paper provides an econometric framework to choose when and how to design the clusters in cluster experiments. Different from existing clustering algorithms geared towards community detection, we motivate the choice of the clusters based on the task of estimating global treatment effects. The choice of clustering must balance two competing objectives: the larger the clusters (and the smaller the number of clusters), the smaller the bias of the estimated global effect, but the larger its variance. We introduce an algorithmic procedure – entitled Causal Clustering – to choose the clustering that minimizes a weighted combination of the worst-case bias and variance as a function of the network and clusters. The worst-case approach encodes uncertainty over the dependence of individual outcomes on neighbors’ assignments. We study (i) whether to run a cluster-level instead of individual-level randomization; (ii) how to cluster individuals (and how many clusters to use). We focus on a class of models where spillover effects are small relative to the outcomes’ variance but possibly non-negligible for inference. This is formalized in a novel framework of local asymptotics where individual outcomes depend arbitrarily on neighbors’ treatments, and, as n grows, spillovers from neighbors (and possibly also direct effects) converge to zero, but at an arbitrary slow rate (e.g., slower than n −1/2 ). This framework encodes the researchers’ uncertainty on the presence (and magnitude) of spillover effects by modeling first-order neighbors’ effect local to zero, with the convergence rate capturing the expected magnitude of spillovers.[2] The local asymptotic framework we study is consistent with settings with small (but non-negligible) treatment and spillover effects, typical, for instance, in online experiments [e.g., Karrer et al., 2021]. We characterize optimal clustering as a function of the expected magnitude of the largest spillover effects that the experiment can generate. The largest size of the spillover effects is a key input in our algorithms, whose characterization, in practice, is necessary for the design of the experiment but can be challenging. This parameter can be informed by previous experiments, in the same spirit of minimum detectable effects used in power analysis [e.g. Baird et al., 2018], or using some particular modeling assumptions. We provide guidance to practitioners in Section 6. Our analysis proceeds as follows. First, we provide a formal characterization of the worstcase bias and variance. We show that the worst-case bias is closely related to a particular notion of between-clusters connectedness, defined as the per-individual average number of friends in other clusters. The worst-case variance can potentially be an arbitrary function of within-clusters covariances and between-clusters covariances: individuals in the same cluster have identical assignments and in different clusters may share common neighbors. We show that the variance only depends on the average squared cluster size, up to an asymptotically negligible error. This result formalizes the intuition that a larger number of clusters, with a small variance in cluster size, decreases the variance of the estimator. We draw the implications of these results for choosing between a cluster experiment (for a given clustering) or assigning treatments independently between individuals (i.e., Bernoulli design or completely randomized design). Suppose the magnitude of the spillover effects is smaller than the square root of the number of clusters. In that case, the variance component dominates the bias, and a Bernoulli design is preferred (where a Bernoulli design is a special case of a cluster design with clusters containing a single unit). Vice-versa, a cluster design is preferred if the bias dominates the variance. Intuitively, because our objective trades off the bias and variance of the estimator, whenever the number of clusters is small, it is best to run a Bernoulli design for any value of spillover effects local to zero. On the other hand, if the number of clusters is sufficiently large, and the cluster design appropriately controls the bias of the estimator, a cluster design is preferred. We provide practitioners with a simple decision rule between cluster and Bernoulli designs that only depends on the number of clusters and the expected spillover effects’ magnitude. We then turn to the design of the optimal clustering, where the choice is not whether to run a cluster or Bernoulli design, but rather which clustering to use. The choice of optimal clustering reduces to a novel penalized minimum cut optimization problem, with a penalty that depends on the variation in clusters’ size. The program admits a convenient formulation as a sequence of trace-optimization problems, each solved via off-the-shelf semidefinite programming algorithms. We provide an empirical application using unique network data from the universe of Facebook users. We show that our procedure provides an explicit ranking between different clustering algorithms implemented at scale at Facebook. We also illustrate trade-offs in the choice of the clustering algorithm and properties of the graph. A second application using network data from Cai et al. [2015] illustrates the method’s advantages for choosing clusters in field experiments. We show that the choice of the clusters based on village identity is sub-optimal in this application, because the number of village clusters is too small relative to the optimal clustering. We present an alternative choice of clusters. This paper connects to the literature on experimental design, causal inference with spillover effects, and clustering. Existing methods for experimental designs with spillover effects include cluster and saturation designs, studied in Baird et al. [2018], Basse and Feller [2016], Karrer et al. [2021], Pouget-Abadie [2018], Taylor and Eckles [2018], Viviano [2020b], among others. These papers provide an analysis of the properties of particular designs for a given clustering or clustering algorithm. Different from the current paper, these references either do not study the question of the optimal clustering algorithm for experimental design, or only provide heuristic comparisons of different clustering methods. A particular class of clustering algorithms is ε-net clustering algorithms – which consist of assigning sequentially individuals in the same neighborhood to the same cluster [Eckles et al., 2017, Ugander et al., 2013]. Variants of these algorithms have been recently studied in Leung [2022] for spatial spillovers and Faridani and Niehaus [2022] in more general nonEuclidean spaces. These papers provide an optimal rate of the clusters’ size as a function of how interference decays in space for approximately unbiased estimators. Here, instead, we provide an explicit trade-off and novel characterization of the bias and variance, leveraging local asymptotics. It allows us to characterize the optimal clustering as the solution of a trace-optimization program (different from ε-net clustering). The comparison of a cluster and Bernoulli designs through local asymptotics is also a novel contribution. Additional references of experiments with networks in the absence of cluster experiments are Basse and Airoldi [2018b], and Jagadeesan et al. [2020] for estimating direct instead of global average treatment effects studied here; Kang and Imbens [2016] who study encouragement designs, without focusing on the problem of mean-squared error optimal designs; Viviano [2020a] for experiments on networks using information from a pilot study; Basse and Airoldi [2018a] discuss limitations of design-based causal inference under interference. None of these paper study (optimal) cluster designs. The literature on treatment effects under network interference includes Aronow and Samii [2017], Hudgens and Halloran [2008], Manski [2013], Leung [2020], Athey et al. [2018], Goldsmith-Pinkham and Imbens [2013], S¨avje et al. [2021], Ogburn et al. [2017], Manresa [2013], Li and Wager [2022], Leung [2022], among others. The assumption that an individual depends on first-order degree connections is most closely related to Leung [2020].3 None of the above references study experimental designs. Finally, we relate more broadly to the literature on clustering in economics – see Wooldridge [2003], Leung [2023], Abadie et al. [2017] and references therein – with the difference that here we focus on optimal clustering, rather than taking the clusters as given – and the literature on graph clustering, including Von Luxburg [2007], Newman [2013a,b], Lei [2019], Lei et al. [2020], Li et al. [2022], and references therein. Different from this last set of papers on graph clustering, this paper focuses on treatment effect estimation, instead of community detection. The remainder of the paper is organized as follows: Section 2 presents the setup; Section
3 characterizes the bias and variance and contrast cluster and Bernoulli designs; Section
4 formalizes the optimization problem for the optimal clustering; Section 5 presents two
applications and numerical studies and Section 6 contains recommendations for practice. [1] For example, when using villages as clusters, individuals may interact in the same and nearby villages. See Egger et al. [2022] for an example in cash-transfer programs. [2] The assumption of spillovers within first-order neighbors can be relaxed here by assuming that higher order spillovers are an order of magnitude smaller than first-order spillovers.

Is GPT Powerful Enough to Analyze the Emotions of Memes?: Abstract & Intro

Across Africa, live without electricity. If you deep it, that's nearly half the entire population sitting in the dark. Even in places with power access, month-long blackouts are common. over 600 million people In the much smaller country of Malawi, have access to the grid. And most of those lucky few live clustered in cities, not scattered across rural areas. only 11% of citizens But One Village Defies the Odds — Bondo Nestled on the slopes of Mount Mulanje, Bondo is a small village that used to live in the dark. For years, it had no hope of getting connected to the national grid. But things changed when Gridless — a Kenyan company that designs, builds, and operates Bitcoin mining sites — installed a micro-hydro mini-grid in the village. The mini-grid harnesses the power of water to generate electricity, which is then used to mine Bitcoin. By mining Bitcoin, Gridless not only earns income to sustain the mini-grid but also provides affordable electricity to over 1,800 homes in Bondo. Erik Hersman, the CEO of Gridless, said: " ." We are not just mining Bitcoin, we are mining hope The electricity has brought many benefits to the villagers. They can now use light bulbs, fridges, TVs, and phones in their homes. They can also start businesses, preserve food, store medicine, and study at night. The women in Bondo are particularly pleased with the development. They told Ian Birrell, the journalist who first reported about the story, that: "Before, our husbands would lie about going to watch football when they were cheating on us. But now with TVs in our homes, they can't use that excuse anymore!” Truly, Africans Can't Use “No Electricity” As an Excuse Anymore Crypto and blockchain adoption is on the rise across Africa. Nigeria has the highest crypto ownership rate in the world, with 47% of its population having used or owned crypto. Kenya, Ghana, and South Africa also rank among the top countries for crypto adoption. Overall, Africa accounts for over $90 billion in cryptocurrency transactions every year. Yet despite this growth, most activity remains concentrated in urban areas, confined to speculative trading rather than real-world usage. And while Bitcoin mining sustainable energy usage globally, stories showcasing this development remain rare in Africa. has reached 54.5% Efforts to expand electrification in Africa have often struggled, as new technologies feel too risky and communities lack the infrastructure to benefit from them. Some blame poor leadership, but the root cause is the systemic underutilization of the continent’s abundant energy resources. Consider these facts: Africa has almost unlimited solar potential, estimated at 11 terawatts, but only a fraction of it is used. It has abundant hydro resources, estimated at 350 gigawatts, but only 5-6% of them are exploited. It has over 100 gigawatts of wind potential, but only 2% of it is utilized. In addition to these untapped resources, the continent boasts a strong human capital, with technical and entrepreneurial talent. African startups raised $5 billion in 2022, a record high. As Malado Kaba, former finance minister of Guinea, put it: "Africa has all the ingredients for an economic breakthrough if it can activate its raw potential." By cleverly tapping into Bitcoin's promise, the village of Bondo has written a script that other African nations can follow. And in doing so, it just might have established a paradox too: How Can an Isolated Village Finance Electricity Access in Ways Even Major Cities Cannot Yet Deliver? The answer lies in innovation - powering a microgrid through Bitcoin mining. Cryptocurrency mining is hugely energy intensive, with Bitcoin alone consuming (TWh) every year - more than the annual consumption of many countries. This intensity stems from Bitcoin's complex Proof of Work (PoW) system. around 143 terawatt-hours In contrast, Sub-Saharan Africa's per capita energy consumption stands at just 180 kWh. Currently, Africa's total electricity demand is 700 TWh, with North African economies and South Africa accounting for over 70%. Mining Bitcoin requires a voracious appetite for energy. The average Bitcoin miner , enough to power a typical American household for six weeks. Yet, despite this demand, major African cities still struggle to meet needs. generates 1,173 kilowatt-hours per transaction South Africa experiences frequent power outages despite having the most electrified grid in Africa. Nigeria's large gas reserves fail to provide stable electricity for its 200 million citizens. Short-term solutions like generators often fail due to high costs. Remarkably, rural villages like Bondo in Malawi have self-financed electricity access in ways megacities cannot. Companies like Gridless have sustainably electrified homes by tapping into Bitcoin mining revenues. The miners secure blockchains while earning enough to maintain village micro-grids. These projects show the benefits of decentralized off-grid solutions for rural electrification. They avoid the high costs and inefficiencies of extending national grids. They also prevent the instability and corruption of state-owned utilities. They use renewable energy sources like micro-hydro, which have less environmental impact than large dams or diesel. And they enable 24/7 mining, which allows for flexible and affordable pay-as-you-go pricing. But Scaling Them Remains Difficult Easier to say what happened in Bondo can happen anywhere else, but the truth is that scaling is difficult. Many villages lack the technical expertise and funding to set up and maintain such systems, while investors are wary of the risks and uncertainties in rural areas. These challenges require the involvement of NGOs, who can help with community education, project development, and capital mobilization. Gridless echoes the need for collaborative approaches to scale these solutions. Eric Hersman was quoted as saying: " We believe that Bitcoin mining can be a catalyst for energy access, but it needs to be done in a way that is respectful of the environment, the culture, and the laws of each country." Beyond Bondo, there are other projects that use digital currencies to drive innovation. In South Africa, Sun Exchange has solar microgrids that allow people to buy and sell solar power using Bitcoin. In Kenya, Powerhive and BitLumens enable pre-paid power meters that accept mobile money and cryptocurrencies. These projects decentralize energy access and empower local communities. Using cryptocurrencies for rural electrification means innovators can also provide banking services and clean energy to the unbanked. However, some regulators have banned or restricted crypto mining, citing environmental and security concerns. These policies ignore the potential benefits of crypto mining for energy access and development and create obstacles to scaling these solutions. For remote villages, crypto mining enables self-reliant development. By using renewable energy sources and digital currencies, they can improve their living standards and economic opportunities, without depending on external aid. Such innovation could also inspire major cities struggling with blackouts and pollution. The Key to Sustainable Adoption of Bitcoin Mining Is in Starting Small Blockchain mining typically means warehouses full of specialized computers consuming vast amounts of electricity. Yet, in off-grid African villages, we find an overlooked opportunity: blockchain technology strategically empowering energy-poor communities. Starting small and local is key to unlocking blockchain's full potential. Too often, innovation targets the ecosystems with the greatest preexisting resources and demand - cities and governments. But the real test comes when expanding access and opportunity to those with the greatest marginalization and need. Companies like Gridless embody this approach. Rather than building urban-first, they establish village-level renewable microgrids able to sustainably mine crypto. This simultaneously earns them income while powering local homes with affordable, off-grid electricity. The community-governed approach offers several benefits: Financial tools for the unbanked through crypto New business opportunities and jobs for entrepreneurs Funding for local health and conservation efforts Most critically, it gives people direct agency rather than relying on centralized authorities. Locals can now self-finance and self-operate the energy infrastructure uplifting their standards of living on their own terms. Scaling this promise requires collaboration. Governments can pilot projects and develop crypt-friendly policy environments. Investors like can participate through sustainable funding models benefitting both communities and shareholders. But at the core, solutions must be co-designed alongside locals rather than imposed top-down by outsiders. GoMining The future looks hard, but signs of hope exist even in darkness. When focused on human needs, blockchains can empower people to uplift themselves. And "starting small" turns sparks into flames. So, while mining may conjure images of warehouses and megawatts, its most pioneering edge may sprout from the overlooked soil of villages. For here, people are positioned to shape their own decentralized energy futures. Note: If you enjoyed this story, please subscribe for alerts on new posts. I'm also open to collaboration :)

No Power Grid? This African Village Mines Bitcoin for Electricity

Authors: (1) Davide Viviano, Department of Economics, Harvard University; (2) Lihua Lei, Graduate School of Business, Stanford University; (3) Guido Imbens, Graduate School of Business and Department of Economics, Stanford University; (4) Brian Karrer, FAIR, Meta; (5) Okke Schrijvers, Meta Central Applied Science; (6) Liang Shi, Meta Central Applied Science. Table of Links Abstract & Introduction Setup (When) should you cluster? Choosing the cluster design Empirical illustration and numerical studies Recommendations for practice References A) Notation B) Endogenous peer effects C) Proofs 5 Empirical illustration and numerical studies In this section we illustrate the properties of the procedure in two empirical applications, one using unique data from the Facebook friendship and messaging graphs, and one using data from an experiment conducted in rural China by Cai et al. [2015]. We provide further evidence supporting our theoretical analysis using simulated networks in Section 5.3. 5.1 Clustering on Facebook graphs We first study the procedure’s properties for two clustering algorithms implemented at scale on social networks owned by Meta: Louvain algorithm [Blondel et al., 2008], Balanced Partitioning [Kabiljo et al., 2017]. We consider two graphs owned by Meta. In each graph, edges are continuous variables. In the first graph, edges capture the strength of the friendship in the Facebook graph, and in the second graph they capture connections based on messaging on Facebook, in both cases the data was aggregated and de-identified. We compute our statistics by setting the edges to be zero if below the 5th, 10th, and 50th percentile. We will refer to these graphs obtained as dense, moderate (mod), and sparse after thresholding (graphs all have a bounded maximum degree). We report the bias and variance, with the variance weighted by a parameter ξn, defined respectively as Louvain and Balanced Partitioning clustering produce a hierarchical clustering structure with a growing number of clusters. We consider three “types” of such algorithms, corresponding to three levels in the clustering structure hierarchy (i.e., different numbers of clusters), defined as Type 1, 2, and 3, respectively. For Louvain clustering, higher-order types denote more clusters, whereas it is the opposite for Balanced Partitioning. For each clustering, we report log(Kn/n), log-ratio of the number of clusters over the population size. Table 2 collects each of these algorithms’ worst-case bias and variance and the number of clusters per individual. Louvain algorithm dominates Balanced Partitioning both in worstcase variance and bias. A larger number of clusters increases the bias but decreases the variance, which we observe throughout all graphs. The bias increases for sparser networks because the denominator |Ni | decreases. Figure 3 reports the objective function for different graphs, Louvain algorithms, and degrees of sparsity. For ξn ≈ 1, the Louvain algorithm with the largest number of clusters (Type 3) dominates the other two algorithms in all but one case. This result provides some interpretable comparisons for the number of clusters. We also observe a trade-off between the number of clusters and the degree of sparsity of the adjacency matrix as ξn decreases. Louvain clustering with the largest number of clusters (Type 1) is optimal for a dense graph with ξn ≫ 1. Such clustering, however, is sub-optimal as the graph becomes more sparse. This result is intuitive: for denser networks, a larger number of clusters may best control the estimator’s bias relative to the bias induced by other clustering algorithms. These comparisons motivate trade-offs in the choice of the graph (and its density) and the clustering algorithm. We recommend practitioners compare clustering by averaging objective functions over different sparsity thresholding. For example, it is possible to compare clustering based on the average objective across different graphs using some priors on the degree of sparsity without affecting our theoretical results. Finally, note that using Theorem 3.6 it is possible to compute the smallest values of the spillover effects that would motivate using a cluster instead of Bernoulli design. From Theorem 3.6, researchers should prefer a cluster over a Bernoulli design if In summary, our results shed light on using clustering algorithms for large-scale implementation on online platforms. These results suggest that Louvain clustering with possibly many clusters performs best in practice. It confirms intuitive arguments in Karrer et al. [2021] who also recommend Louvain clustering based on AA-tests. 5.2 Clustering in the field We consider as an application the problem of informing individuals to increase insurance takeup studied in Cai et al. [2015]. The authors collected network information in approximately 184 villages in rural China from 48 larger regions. We use network data collected by Cai et al. [2015] to study the properties of the proposed method, where we assume that two individuals are connected if at least one of the two indicates the other as a friend. Because we do not require information from end-line outcomes, we construct a network using information from surveyed individuals as well as their friends. The network has in total 7649 nodes, once we also include individuals who are friends of surveyed individuals (but do not necessarily live in their same village or were surveyed in the experiment). Individuals between villages present on average 50% of their connections outside their village. On the other hand, individuals in different regions (with approximately 50 regions) have 99% of their connections within the same region. We use an adjacency matrix a “weak ties” adjacency matrix, where two individuals are connected if either indicates the other has a friend. We study clustering within each region and report results for different clustering algorithms. Clustering within each region is performed as in Algorithm 1, where we first estimate Xˆ (K) via semi-definite programming, use K-means algorithm to retrieve the clusters from Xˆ (K) and iterate to estimate the optimal number of clusters K. We report the frontier with respect to the absolute bias consistently with Algorithm 1. 5.2.1 How many clusters should we choose? Figure 4 presents the main results. The left-hand side panel reports the average number of estimated clusters, estimated by the proposed method divided by the population size in a given cluster. Different choices of ξn justify a different number of clusters. For ξn = 1, the number of clusters is 7%, the total population size, whereas for ξn = 15, the number of clusters is roughly half the population size. To gain further intuition of its practical implication, take ψ¯ = 0.24 approximately equal to the outcomes’ variance in Cai et al. [2015] and ϕ¯ n = 0.27 as in Table 2 in Cai et al. [2015]. A recommended choice of ξn is approximately ψ/¯ ϕ¯2 n = 3.29. This choice puts slightly more weight on the bias than the variance when λ = 1, and therefore can be considered a conservative choice for the number of clusters.[13] Even with such a conservative choice, the suggested number of clusters is large relative to the population size. In this case, the optimal number of clusters is around 15% of the overall population size, approximately 600 clusters with a surveyed sample size of 3600 individuals. This number is consistent (in scale) with the choice in the number of clusters in other applications in development, such as Egger et al. [2022], Alatas et al. [2012], but larger than the number of villages that we have in the application in Cai et al. [2015]. 5.2.2 Comparison with other clustering methods Next, we compare the procedure (denoted as “Causal Clustering”) with the following alternative clustering algorithms: ε-net clustering as in Eckles et al. [2017] with ε = 3 as suggested in Eckles et al. [2017]; spectral clustering with a fixed number of clusters equal to n/3, where n is the population size in a given region (note that spectral clustering do not optimize over the number of clusters); Louvain clustering with default parameters selected by the R-package igraph; clustering based on village identity of each individual (creating one additional cluster for those individuals whose village is missing in the data). We contrast the objective function with alternative algorithms in the right-hand side of Figure 4. The proposed method achieves the smallest objective function across all competitors, providing suggestive evidence that Algorithm 1 (with a semi-definite relaxation) works particularly well in practice. For a smaller number of clusters the method is closer to the Louvain clustering algorithm, while for a larger number of clusters the objective of the algorithm is close to the objective of the spectral algorithm. Interestingly, clustering based on village identity leads to the largest loss function. This is because individuals are connected within and between villages in this application. Finally, in Figure 5 (first three panels), we report the mean-squared error obtained by simulating the model in Cai et al. [2015] (Table 2, Column 4) and vary the variance of the residuals σ 2 ∈ {1/4, 1/2, 1} to emulate settings with high, medium and low signal to noise ratio. The model in Cai et al. [2015] assumes that individual outcomes depend on neighbors’ treatments, consistently with our Assumption 1. Our method uniformly outperforms competitors in terms of mean-squared error, except for the Louvain algorithm when the signal-to-noise ratio is particularly high, but not in the remaining settings. In the last panel, we consider settings where the local interference assumption is violated, and we calibrate our model to the specification in Cai et al. [2015] (Column 4, Table 5), where the individual outcome depends on the neighbors’ outcomes, violating Assumption 1 and introducing global dependence. We fix the residuals’ variance to be σ 2 = 1/4, whereas the results are robust as we increase σ 2 . In this setup, we observe a larger mean-squared error of all methods, with the proposed method achieving the lowest mean-squared error. 5.3 A numerical study Next, we illustrate the properties of the method in numerical studies. We consider three different data generating processes for the network formation: a geometric network formation, Albert Barabasi network and Erdos-Renyi graph. The geometric network takes the form Ai,j = 1{|Xi,1 − Xj,1|/2 +|Xi,2 − Xj,2|/2 ≤ rn} where rn = p 4/2.75n similarly to simulations in Leung [2020]. Here, Xi,1, Xi,2 are drawn independently from a uniform distribution between [−1, 1]. For the Albert-Barabasi network we first draw n/5 edges uniformly according to Erdos-Renyi graph with probabilities p = 10/n, and second, we draw sequentially connections of the new nodes to the existing ones with probability equal to the number of connection of each pre-existing node divided by the overall number of connections. Finally, the Erdos-Renyi graph is generated with probability of connection p = 2/n. We study the properties of the proposed method and of the same competitors discussed in Section 5.2. Figure 6 showcases that the proposed procedure leads to the lowest objective function. This result is consistent with our theoretical findings. Figure 7 showcases that the number of clusters increases in the sample size, varies substantially across different data generating processes, and increases in ξn. [13] This follows from the fact that ξn is proportional to ψ/¯ ϕ¯2 n for clusters whose bias is of order O(ϕ¯ n), and researcher’s goal is to minimize the worst-case mean squared error (λ = 1). This paper is under CC 1.0 license. available on arxiv

Design of Cluster Experiments: Empirical Illustration and Numerical Studies

Authors: (1) Davide Viviano, Department of Economics, Harvard University; (2) Lihua Lei, Graduate School of Business, Stanford University; (3) Guido Imbens, Graduate School of Business and Department of Economics, Stanford University; (4) Brian Karrer, FAIR, Meta; (5) Okke Schrijvers, Meta Central Applied Science; (6) Liang Shi, Meta Central Applied Science. Table of Links Abstract & Introduction Setup (When) should you cluster? Choosing the cluster design Empirical illustration and numerical studies Recommendations for practice References A) Notation B) Endogenous peer effects C) Proofs Abstract This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of spillovers on a single network. We provide an econometric framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global treatment effect. We show that the optimal clustering can be approximated as the solution of a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes easy-to-check conditions to choose between a cluster or individual-level randomization. We illustrate the method’s properties using unique network data from the universe of Facebook’s users and existing network data from a field experiment. Experimental Design, Spillover Effects, Causal Inference, Cluster Designs. JEL Codes: C10, C14, C31, C54 Keywords: 1 Introduction Consider a (large) population of n individuals connected under a single observed network. Researchers are interested in conducting an experiment to estimate the global average treatment effect, i.e., the difference between the average effect of treating all versus none of the individuals in the population. Treating an individual may generate spillovers to her friends in the network. To capture such effects, researchers conduct a cluster experiment. Individuals are first partitioned into clusters. Within a cluster, either all units are assigned to the treatment or all units are assigned to the control group. Finally, researchers estimate treatment effects by taking a difference between the average outcomes of treated and control units (possibly adjusting for baseline covariates). The cluster design does not require modeling the dependence of individual outcomes on neighbors’ assignments, but it requires a choice of clusters and some assumptions on the extend of the spillovers along the network. For example, cluster experiments on online platforms require choosing a partition of the social network, and field experiments require choosing the unit of randomization, such as villages or regions. This raises the question of how many and which clusters to use in experiments. Typical approaches in economic research assume prior knowledge of many independent clusters. There are many settings where this information is not available, and instead units in the population have different degrees of connections.[1] This paper provides an econometric framework to choose when and how to design the clusters in cluster experiments. Different from existing clustering algorithms geared towards community detection, we motivate the choice of the clusters based on the task of estimating global treatment effects. The choice of clustering must balance two competing objectives: the larger the clusters (and the smaller the number of clusters), the smaller the bias of the estimated global effect, but the larger its variance. We introduce an algorithmic procedure – entitled Causal Clustering – to choose the clustering that minimizes a weighted combination of the worst-case bias and variance as a function of the network and clusters. The worst-case approach encodes uncertainty over the dependence of individual outcomes on neighbors’ assignments. We study (i) whether to run a cluster-level instead of individual-level randomization; (ii) how to cluster individuals (and how many clusters to use). We focus on a class of models where spillover effects are small relative to the outcomes’ variance but possibly non-negligible for inference. This is formalized in a novel framework of local asymptotics where individual outcomes depend arbitrarily on neighbors’ treatments, and, as n grows, spillovers from neighbors (and possibly also direct effects) converge to zero, but at an arbitrary slow rate (e.g., slower than n −1/2 ). This framework encodes the researchers’ uncertainty on the presence (and magnitude) of spillover effects by modeling first-order neighbors’ effect local to zero, with the convergence rate capturing the expected magnitude of spillovers.[2] The local asymptotic framework we study is consistent with settings with small (but non-negligible) treatment and spillover effects, typical, for instance, in online experiments [e.g., Karrer et al., 2021]. We characterize optimal clustering as a function of the expected magnitude of the largest spillover effects that the experiment can generate. The largest size of the spillover effects is a key input in our algorithms, whose characterization, in practice, is necessary for the design of the experiment but can be challenging. This parameter can be informed by previous experiments, in the same spirit of minimum detectable effects used in power analysis [e.g. Baird et al., 2018], or using some particular modeling assumptions. We provide guidance to practitioners in Section 6. Our analysis proceeds as follows. First, we provide a formal characterization of the worstcase bias and variance. We show that the worst-case bias is closely related to a particular notion of between-clusters connectedness, defined as the per-individual average number of friends in other clusters. The worst-case variance can potentially be an arbitrary function of within-clusters covariances and between-clusters covariances: individuals in the same cluster have identical assignments and in different clusters may share common neighbors. We show that the variance only depends on the average squared cluster size, up to an asymptotically negligible error. This result formalizes the intuition that a larger number of clusters, with a small variance in cluster size, decreases the variance of the estimator. We draw the implications of these results for choosing between a cluster experiment (for a given clustering) or assigning treatments independently between individuals (i.e., Bernoulli design or completely randomized design). Suppose the magnitude of the spillover effects is smaller than the square root of the number of clusters. In that case, the variance component dominates the bias, and a Bernoulli design is preferred (where a Bernoulli design is a special case of a cluster design with clusters containing a single unit). Vice-versa, a cluster design is preferred if the bias dominates the variance. Intuitively, because our objective trades off the bias and variance of the estimator, whenever the number of clusters is small, it is best to run a Bernoulli design for any value of spillover effects local to zero. On the other hand, if the number of clusters is sufficiently large, and the cluster design appropriately controls the bias of the estimator, a cluster design is preferred. We provide practitioners with a simple decision rule between cluster and Bernoulli designs that only depends on the number of clusters and the expected spillover effects’ magnitude. [1] For example, when using villages as clusters, individuals may interact in the same and nearby villages. See Egger et al. [2022] for an example in cash-transfer programs. This paper is under CC 1.0 license. available on arxiv

Causal Clustering: Design of Cluster Experiments Under Network Interference

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