Cities, public authorities, and private organizations respond to climate change with various green policies and strategies to enhance community resilience. However, these community-level transition processes are complex and require deliberate and collective planning. Under this context, the purpose of this study is to understand the energy actions taken at the local level, as well as to analyze the differences between the neighborhoods’ green energy transitions in terms of their socio-economic aspects, using a big data perspective. The paper is addressing the following question: what was the role that the pandemic played in accelerating or slowing Boston’s green investments, and to what extent do different racial and socioeconomic groups invest in green technologies during this period? The study aims to answer these research questions using the City of Boston as a case study to reveal different neighborhoods’ paths in achieving the transformation of city ecosystems towards green neutrality. Next, the theoretical framework builds the linkages among the city’s measures, climate actions proposed by the City of Boston, and their associated contexts and outcomes in shaping new policy and planning models for higher ‘green’ performance. Following the understanding of the actions, the neighborhoods’ socio-economic and building permit data were assessed to understand whether economic disparities exacerbated during the pandemic have affected neighborhoods’ performance in green transition. This method is applied in a comparative study of its 23 neighborhoods, using a dataset provided by Boston Area Research Initiative (BARI). Intriguingly, the paper’s findings show that racial differences within the city have no significant impact on tech-related expenditures. There is a clear negative correlation between poverty rate and investment, which indicates the reverse relationship between these socio-economic factors. The study concludes that city authorities will need to address the challenges of each community achieving green transition with more targeted programs based on its needs.
