Evaluating the role of government expenditure in promoting renewable energy and economic growth in India

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The transition to a sustainable energy economy requires substantial public investment, with government spending playing a crucial role in driving the adoption of renewable energy and achieving environmental outcomes. This study investigates the impact of India’s budgetary allocations on renewable energy consumption, carbon emissions, and economic growth. The analysis covers annual data from 1990 to 2024. It employs the autoregressive distributed lag (ARDL) bounds testing approach and the Granger causality test to examine long-term equilibrium relationships and directional causality among the variables. The results indicate a statistically significant long-run relationship between government expenditure, renewable energy usage, and carbon emissions. Specifically, a 1% increase in renewable energy consumption (REC) results in a 1.14% decrease in carbon emissions, demonstrating the environmental benefits of clean energy deployment. The ARDL model also shows that past government disbursements significantly contribute to emissions reduction, with coefficients of –2147.41 (p < 0.001) and –997.36 (p < 0.05) at lags one and two, respectively. Granger causality results confirm an unidirectional causal relationship between renewable energy expenditures (REE) and carbon emissions, as well as between government spending and gross domestic product (GDP), highlighting the dual impact of such investment on environmental sustainability and economic growth. The findings underscore the effectiveness of public financial support in accelerating the transition to renewable energy while advancing macroeconomic goals. Strengthening and sustaining government investment in renewable energy is essential for achieving India’s long-term development targets, reducing carbon intensity, and promoting green economic growth.

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    • Table 1. Unit root test (ADF) (Null hypothesis: The variable has a unit root)
    • Table 2. Autoregressive distributed lag model for estimating the impact of renewable energy consumption on CO₂ emissions
    • Table 3. Bounds test for cointegration in the ARDL model for renewable energy consumption and CO2 emissions
    • Table 4. Error correction model (VECM) for renewable energy consumption and CO2 emissions
    • Table 5. Autoregressive distributed lag model for CO2 emissions and renewable energy expenditures
    • Table 6. Bounds test for cointegration in the ARDL model for renewable energy expenditures and CO2 emissions
    • Table 7. Granger causality test for CO2 emissions, REC and REE
    • Conceptualization
      Karen Fernandes, Shripad Ramchandra Marathe
    • Formal Analysis
      Karen Fernandes, Sanjeeta Parab, Virendra Amonkar, Gauri Vernekar, Sunny Sonu Pandhre
    • Investigation
      Karen Fernandes, Shripad Ramchandra Marathe, Virendra Amonkar
    • Supervision
      Karen Fernandes
    • Writing – original draft
      Karen Fernandes, Shripad Ramchandra Marathe, Sanjeeta Parab, Virendra Amonkar, Gauri Vernekar, Sunny Sonu Pandhre
    • Writing – review & editing
      Karen Fernandes, Shripad Ramchandra Marathe, Sanjeeta Parab, Virendra Amonkar, Gauri Vernekar, Sunny Sonu Pandhre
    • Data curation
      Shripad Ramchandra Marathe, Virendra Amonkar, Gauri Vernekar, Sunny Sonu Pandhre
    • Methodology
      Shripad Ramchandra Marathe, Sanjeeta Parab, Virendra Amonkar, Gauri Vernekar, Sunny Sonu Pandhre
    • Validation
      Shripad Ramchandra Marathe, Sanjeeta Parab, Gauri Vernekar, Sunny Sonu Pandhre
    • Visualization
      Shripad Ramchandra Marathe, Gauri Vernekar, Sunny Sonu Pandhre