This study uses a spatial Durbin error model (SDEM) approach to analyze adoption trends for residential energy-efficiency measures (EEMs) in New York state. Model results are based on socioeconomic, building, and household demographic characteristics during the 2012–2016 period. Our study’s results confirm that a positive correlation exists between EEM uptake and multifamily buildings, gas-heated homes, education effects, and spatial spillover effects among neighboring ZIP codes. The results show that building attributes hold a relatively high explanatory power over EEM adoption compared with socioeconomic characteristics. Our results show that energy-efficiency policies can create positive and significant neighborly effects in promoting EEM adoption. The developed SDEM methodological framework provides useful insights in identifying energy-efficiency opportunities that exist in rural, suburban, and urban communities, highlighting the need to review policy incentives periodically to address underlying changes in the built environment and spatial disparities in energy-efficiency investments.

Expansion of distributed solar photovoltaic (PV) and natural gas‐fired generation capacity in the United States has put a renewed spotlight on methods and tools for power system planning and grid modernization. This article investigates the impact of increasing natural gas‐fired electricity generation assets on installed distributed solar PV systems in the Pennsylvania–New Jersey–Maryland (PJM) Interconnection in the United States over the period 2008–2018. We developed an empirical dynamic panel data model using the system‐generalized method of moments (system‐GMM) estimation approach. The model accounts for the impact of past and current technical, market and policy changes over time, forecasting errors, and business cycles by controlling for PJM jurisdictions‐level effects and year fixed effects. Using an instrumental variable to control for endogeneity, we concluded that natural gas does not crowd out renewables like solar PV in the PJM capacity market; however, we also found considerable heterogeneity. Such heterogeneity was displayed in the relationship between solar PV systems and electricity prices. More interestingly, we found no evidence suggesting any relationship between distributed solar PV development and nuclear, coal, hydro, or electricity consumption. In addition, considering policy effects of state renewable portfolio standards, net energy metering, differences in the PJM market structure, and other demand and cost‐related factors proved important in assessing their impacts on solar PV generation capacity, including energy storage as a non‐wire alternative policy technique.This article is categorized under: Photovoltaics > Economics and Policy Fossil Fuels > Climate and Environment Energy Systems Economics > Economics and Policy