Com a eletricidade vem a prosperidade, certo? A resposta pode parecer óbvia: a eletricidade é fundamental para o crescimento econômico e outros benefícios sociais, como uma saúde melhor. Mas nas regiões mais empobrecidas do mundo...
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)
Quase 600 milhões de pessoas na África subsaariana não tinham acesso à eletricidade baseada na rede central em 2018. Neste mapa, os países são coloridos pelo número de pessoas sem acesso à eletricidade. Uganda, com cerca de 24 milhões de pessoas sem rede elétrica em 2018, é destacada por uma borda vermelha. Crédito: Ratledge et al, 2022, Nature
Com a eletricidade vem a prosperidade, certo? A resposta pode parecer óbvia: a eletricidade é fundamental para o crescimento econômico e outros benefícios sociais, como uma saúde melhor. Mas nas regiões mais empobrecidas do mundo, onde os recursos para melhorar os meios de subsistência são escassos, não está claro o quanto as pessoas ficam em melhor situação quando têm acesso à energia elétrica.
Agora, no entanto, um novo estudo de Stanford publicado em 16 de novembro na revista Nature revela algumas das evidências mais fortes e diretas da extensão em que a eletrificação alimenta o crescimento econômico no mundo em desenvolvimento. A pesquisa é a primeira a contar com uma técnica pioneira recentemente desenvolvida em Stanford que combina imagens de satélite e inteligência artificial (IA) para medir e estudar a pobreza de maneiras que antes não eram possíveis.
O estudo – coautoria de Marshall Burke, professor associado da Stanford Doerr School of Sustainability e membro sênior do Stanford Institute for Economic Policy Research (SIEPR); Nathan Ratledge, um Ph.D. estudante em meio ambiente e recursos na Doerr School; e outros — examinaram os impactos econômicos da expansão da rede elétrica de Uganda .
Os pesquisadores descobriram que as comunidades com acesso à eletricidade experimentaram melhorias em seus meios de subsistência econômica aproximadamente o dobro das regiões sem energia. Eles medem as mudanças nos meios de subsistência com base nos aumentos que identificam na construção de casas, eletrodomésticos e outros bens tangíveis sugestivos de riqueza econômica.
“Fornecemos evidências causais inéditas de como o acesso à eletricidade afeta o bem-estar econômico em escala em um país inteiro na África”, disse Ratledge, principal autor do estudo.
O estudo surge quando a situação do mundo em desenvolvimento ocupou o centro do palco em uma reunião de mudanças climáticas das Nações Unidas na semana passada de líderes mundiais no Cairo. Cerca de 600 milhões de pessoas na África subsaariana ainda não têm acesso à eletricidade, observou Ratledge. Em Uganda, um dos países mais pobres do mundo , a rede elétrica cobria 41% de sua massa terrestre em 2019 – acima dos 12% em 2010.
Abrindo possibilidades de pesquisa para informar a política
Quando Ratledge procurou estudar a eletrificação na África pela primeira vez, cinco anos atrás, ele se deparou com um obstáculo comum: ele não conseguia encontrar mapas de redes elétricas em nenhum lugar do continente que lhe permitissem analisar o progresso ao longo do tempo. “É difícil em muitos países de baixa renda obter dados confiáveis ??e, especialmente, dados repetidos ao longo do tempo”, disse ele. "Em muitos casos, simplesmente não existe."
Por acaso, Burke - junto com David Lobell, membro sênior do SIEPR e professor de ciência do sistema terrestre; e Stefano Ermon, professor associado de ciência da computação – estava trabalhando em uma nova ferramenta que prometia resolver essa lacuna de dados e outros problemas que os economistas do desenvolvimento encontram ao tentar entender como a pobreza muda com o tempo.
Sua técnica gira em torno do "aprendizagem profunda" - neste caso, uma forma de IA na qual um algoritmo foi treinado para detectar padrões e extrair informações de imagens - que os estudiosos aplicam a imagens de satélite de acesso livre que remontam ao longo do tempo. Os pesquisadores aplicaram essa abordagem para estudar a riqueza de ativos no nível da comunidade em toda a África, que é uma forma comum de medir o bem-estar econômico nas nações em desenvolvimento. Burke e seus colaboradores detalharam sua inovação em um artigo de 2020 na Nature Communications .
O estudo de Uganda marca a primeira vez que a técnica foi usada para avaliar o impacto de uma política específica – uma capacidade que os pesquisadores há muito esperavam que se concretizasse. Nesse caso, Ratledge e seus coautores se concentram na rápida expansão da rede elétrica de Uganda em 2011 e 2012. Para estudar seus efeitos ao longo do tempo, eles combinaram os mapas digitalizados recém-desenvolvidos da rede elétrica do país de 2005 a 2016 com imagens de satélite. baseou estimativas de riqueza de um modelo de " aprendizagem profunda " que foi treinado em dados que abrangem cerca de 642.000 famílias em 27.000 aldeias na África subsaariana.
Os pesquisadores descobriram que as comunidades que tiveram acesso à eletricidade aumentaram sua riqueza cerca de duas vezes mais do que aquelas onde a energia não estava disponível.
"Esta percepção não teria sido possível apenas alguns anos atrás", disse Burke. “E é porque agora temos essa técnica para fornecer medições em nível local dos principais resultados econômicos em uma ampla escala espacial e ao longo do tempo”.
Embora a técnica ainda seja um tanto nova, Burke prevê que os avanços em andamento na computação ultrapoderosa e barata logo a tornarão prontamente acessível a pesquisadores que estudam políticas e programas em qualquer país, rico ou pobre, com o objetivo de reduzir a pobreza. Pense, por exemplo, nas políticas que afetam a agricultura, a saúde e o desenvolvimento de infraestrutura.
Ratledge concorda. "Esta técnica abre uma fronteira totalmente nova e dramaticamente diferente para avaliar o crescimento econômico entre os países emergentes", disse ele. "É incrível, na verdade."
Além de Ratledge e Burke, os coautores do estudo de Uganda foram Gabriel Cadamuro, engenheiro sênior de aprendizado de máquina da Atlas AI, uma empresa de análise preditiva cofundada por Burke, Lobell e Ermon para apoiar o desenvolvimento global ; Brandon de la Cuesta, pesquisador afiliado ao King Center on Global Development e pós-doutorado no Stanford Center for Democracy, Development and the Rule of Law e no Center on Food Security and the Environment; e Matthieu Stigler, ex-colega de pós-doutorado de Stanford no Centro de Segurança Alimentar e Meio Ambiente, que agora é pesquisador da ETH Zurich.
Mais informações: Marshall Burke, Usando aprendizado de máquina para avaliar o impacto do acesso à eletricidade nos meios de subsistência, Nature (2022). DOI: 10.1038/s41586-022-05322-8 . www.nature.com/articles/s41586-022-05322-8
Informações do jornal: Nature , Nature Communications