U.S. State-level Renewable Energy Production (2020-2023) Econometric Geospatial Dataset

This dataset contains raw and analyzed data. The analyzed dataset is derived from a cross-sectional panel of all 50 U.S. states, constructed by averaging annual values from 2020–2023 to smooth short-term volatility. The primary outcome /dependent variable is renewable energy production per capita (Renew_prod_pc, Btu per person), transformed using a log1p function (log_Renew_prod_pc) to address skewness and enable elasticity-based interpretation. Key predictors capture demographic, economic, political, policy, and energy-structure characteristics. State size and economic scale are controlled using logged population (log_Population) and logged gross state product per capita (log_Gdp_pc). Policy context is measured by the count of renewable energy incentives (Policy_Incentives_count) from DSIRE and partisan control of state government (Political_persuasion, binary). Regional and structural factors include Appalachian designation (Appa_st), coal production per capita, renewable energy consumption per capita, and the number of active coal and renewable power plants. Additional controls describe industrial composition (Gdp_oilgas_share, Gdp_mfg_share), labor market conditions (Unemp_rate), human capital (Pop_18_25_lt9_pct), and socioeconomic vulnerability (Poverty_rate). All variables are state-level and sourced from authoritative U.S. agencies (EIA, BEA, Census Bureau, BLS) or established policy databases. The analytical outputs include: (1) Bayesian regression parameter estimates (posterior means, SDs, 95% HDIs, R-hat, ESS, and credibility). (2) Model-level comparison statistics across alternative specifications (Full, Parsimonious, Minimal), including Bayesian R² and LOO-ELPD. (3) Spatial diagnostics, comprising bivariate Moran’s I statistics and Local Indicators of Spatial Association (LISA) cluster summaries and state-level classifications.