Macroeconomic Environmental Interactions and the Path of India’s Energy Transition

Macroeconomic Environmental Interactions and the Path of India’s Energy Transition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Macroeconomic Environmental Interactions and the Path of India’s Energy Transition Agnus Infanta B, JAYANTHI M This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8720414/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract India’s economic growth has long been intertwined with environmental challenges, particularly as it seeks to align development goals with climate commitments under the Paris Agreement. Despite increasing investments in green finance and renewable energy, structural barriers persist, limiting the pace of energy transition. This paper provides new empirical insights into the dynamic interactions between macroeconomic, environmental, and energy variables in India using a Bayesian Vector Autoregression (BVAR) framework over the period 2000–2020. Major findings include stagnant renewable energy consumption (REC) through 2027 amid rising CO₂ emissions and declining R&D spending, a negative impact of PM2.5 pollution and CO₂ emissions on REC, and strong path dependency in REC trends. Impulse response functions reveal that foreign direct investment (FDI) initially suppresses but later promotes REC, while paradoxical effects between R&D spending and environmental trade highlight misaligned policy incentives. To support India’s Viksit Bharat 2047 vision, the study recommends targeted reforms, including redirecting FDI toward renewable projects, boosting innovation funding for energy technologies, and realigning environmental trade policies to enhance energy transition outcomes. JEL Classification : E31, Q54, C32, O13 BVAR renewable energy India climate policy macroeconomic forecasting Figures Figure 1 Figure 2 I. INTRODUCTION India’s economic development path is progressively shaped by the twin imperatives of industrialization and environmental sustainability. Being one of the world’s largest greenhouse gas emitters, India plays a pivotal role in global climate action through its commitments under the Paris Agreement (UNFCCC, 2015). The country vowed to reduce emissions intensity by 45% from 2005 levels by 2030 and to achieve net-zero emissions by 2070 (Government of India, 2021; IMF, 2024). These areas are dominant to India’s Viksit Bharat 2047 vision, which visualizes a future where economic growth is distinguished from environmental degradation. Even with increasing investments in green finance and renewable energy there exists a structural barrier limiting the renewable energy adoption (Cui et al., 2023; Liu et al., 2023). Green finances emerged as a key mechanism for promoting clean energy adoption and reducing carbon footprints (Zhang et al., 2022). However empirical evidence estimating how financial flows, macroeconomic indicators, and environmental pressures interact to shape India’s energy transition remains partial (IMF, 2024). Most studies focus on aggregate impacts without accounting for feedback loops or forecasting future trends under current conditions (Jafar, 2000). The turning point in global climate governance, came with the signing of the Paris Agreement in COP21 in 2015, which highlighted the need for countries to shift towards low carbon economies and enhance climate resilience through sustainable financial systems (Yang & Park, 2020). India sanctioned the agreement in 2016, confirming its commitment to sustainable development by aligning it with Mahatma Gandhi’s birth anniversary, representing the moral imperative of ecological stewardship (Government of India, 2021). At the domestic level, the period from 2000 to 2020 was marked by significant transformation in India’s environmental and economic policies. While the economy grew rapidly, challenges such as rising CO₂ emissions, air pollution, and energy insecurity persisted (Chakraborty & Mukherjee, 2010; Singh & Jha, 2019). Important commitments include increasing the share of non-fossil fuel-based electricity generation capacity to 50% by 2030 and enhancing forest cover to create additional carbon sinks (Roemer & Haggerty, 2021). Understanding the dynamic interlinkages between energy use, economic indicators, and environmental impacts is crucial for formulating effective policies that support long-term sustainability (IMF, 2024). To address this gap, our approach builds on earlier studies using multivariate time series models to explore energy, environment & economy linkages (Sims, 1980; Koop et al., 1996; Kilian & Lutkepohl, 2017). We apply a Bayesian Vector Autoregression (BVAR) model to assess the impact of eight key indicators - renewable energy consumption (REC), CO₂ emissions, PM2.5 levels, foreign direct investment (FDI), R&D expenditure, labour force participation, inflation, and environmental trade on REC over the period 2000 to 2020. We extend the analysis by forecasting trends up to 2027, offering novel insights into policy dynamics to enhance REC in alignment with India’s Viksit Bharat 2047 vision and Paris Agreement renewable energy goals. Renewable energy consumption (REC), defined as the share of renewable energy in total final energy consumption, is selected as the focal variable due to its direct relevance to India’s commitment to achieve 50% non-fossil fuel-based electricity capacity by 2030 and net-zero emissions by 2070 (Government of India, 2021). II. LITERATURE REVIEW While the dynamic interlinkages between energy use, economic indicators, and environmental impacts have been widely studied for India and similar economies, this section reviews key prior research thematically to highlight gaps addressed by the current study. Environmental Determinants of Renewable Energy Adoption Aguirre and Ibikunle (2014) employ panel data regression analysis on a global sample to identify the primary economic, institutional and policy factors influencing renewable energy growth. Their use of fixed effects panel regressions allows them to control for unobserved heterogeneity and robustly identify determinants operating across countries. Hoang et al. (2024) employ time-series and causality modelling to dissect the dynamic relationships linking economic growth, energy use and CO₂ emissions, revealing bidirectional causalities and underscoring the necessity for integrating renewables to achieve conjoined economic-environmental objectives. Dogan and Seker (2018) analyse the drivers of renewable energy development across European Union countries using panel cointegration techniques. Their empirical strategy includes panel unit root tests, cointegration tests and fully modified ordinary least squares (FMOLS) estimation, thus capturing both the presence of long-term relationships and the magnitude of individual factors across the EU. Ozturk and Saboori (2021) utilize an autoregressive distributed lag (ARDL) bounds testing approach to examine the short- and long-run impacts of policy, economic and social variables on renewable energy consumption in the United States. This method enables an explicit assessment of both immediate and persistent effects of key determinants. Economic Enablers of Energy Transition Ewah et al. (2021) investigate how digitalisation acts as a catalyst for sustainable energy transitions in African economies, particularly Nigeria and South Africa, illustrating the impact of smart infrastructure and digital platforms on sectoral transformation. Innovation and Trade in Sustainable Energy Systems Shahbaz et al. (2024) utilize panel econometric modelling to examine the relationship between renewable energy finance, economic development and environmental outcomes in the OECD and BRICS countries, underscoring the pivotal role of financial flows and governance quality in shaping transition pathways. In the Belt and Road Initiative context, Liu et al. (2023) employ the CS-ARDL framework to reveal the asymmetric effects of financial openness, trade integration and capital formation on energy sustainability, exposing complex cross-border interdependencies. Zhao et al. (2024) demonstrate, through transmission mechanism modeling, that digital innovation directly strengthens both the financial resilience and green transition capability of energy companies, substantiating the value of digital investments for sustainability performance. Likewise, Qureshi et al. (2024) explore how financial inclusion, digital strategies and governance converge to enhance environmental performance across OECD nations, arguing for the mainstreaming of digital innovation within policy frameworks. Meanwhile, Lin et al. (2024) uniquely approach “multidimensional green growth” using scenario analysis and panel data to link targeted green finance and innovation policy to sustainability performance across economic cycles. Theoretical Frameworks of Sustainability Transitions Sustainability transition scholarship has expanded rapidly over recent years, offering insights relevant to the green growth and renewable energy transitions of emerging economies. For instance, Markard (2024) presents a comprehensive analysis of sustainability transitions in the twenty-first century, highlighting the intertwined roles of innovation, systemic transformation and policy design across global contexts. Complementing this focus, Mahmood et al. (2022) deploy science mapping and co-word analysis to delineate evolving thematic concentrations in renewable energy research, revealing a pronounced shift toward interdisciplinary integration and alignment with the Sustainable Development Goals. Loorbach et al. (2017) build on this by conceptualizing sustainability transitions as processes deeply rooted in socio-technical, institutional and ecological change, emphasizing the crucial interplay between deliberate governance and spontaneous market dynamics. Collectively, these studies underscore that successful energy and sustainability transitions are multidimensional phenomena, shaped by contextual drivers ranging from technological capability and digitalisation to financial architecture and policy commitment. Together, they provide a holistic scholarly backdrop that both situates and justifies further research employing dynamic, country-specific modelling as in the present Indian case. However, they lack dynamic scenario modelling and the ability to capture feedback effects among variables. Unlike the static panel regressions of Aguirre and Ibikunle (2014) or the ARDL approach of Ozturk and Saboori (2021), which fail to account for interdependent dynamics, this study employs a Bayesian Vector Autoregression (BVAR) model tailored to the Indian context to overcome these shortcomings. Additionally, while Dogan and Seker (2018) rely on cointegration techniques to explore long-term relationships across the European Union without forward-looking analysis, this study extends the analysis with forecasts up to 2027, addressing this gap with a perspective tailored to India’s unique policy landscape, including its Viksit Bharat 2047 vision and Paris Agreement commitments. Furthermore, the inclusion of policy-relevant variables such as environmental trade and an emphasis on lagged effects, which are absent in the broader regional or global analyses of these prior works, enhance the complex understanding of India’s energy transition provided by the BVAR approach. III. METHODOLOGY The selection of the Bayesian Vector Autoregression (BVAR) model is strategically justified by its ability to address the methodological challenges posed by this study’s dataset and research objectives. With only 21 annual observations (2000–2020), classical VAR models risk overfitting due to their reliance on unrestricted parameter estimation, whereas BVAR’s incorporation of informative priors, such as the Minnesota prior, stabilizes estimates by leveraging theoretical expectations about lag structures and variable relationships. Compared to the Autoregressive Distributed Lag (ARDL) approach, which is effective for cointegration but less suited for capturing multivariate feedback loops, BVAR excels in tracing impulse responses across REC, environmental and economic variables. Panel methods, while robust for cross-country analysis, fail to account for India-specific dynamics, such as policy shocks from the Paris Agreement or regional coal dependency. BVAR’s suitability is further validated by its capacity to handle nonlinear lagged effects and small-sample uncertainty, making it ideal for modeling India’s energy transition. This approach enables robust forecasting up to 2027, aligning with the Viksit Bharat 2047 vision and supports the study’s goal of integrating advanced econometrics with policy-relevant insights. This methodological advantage underpins the detailed implementation of BVAR, which leverages prior information to enhance estimation accuracy in India’s constrained data environment. The BVAR framework allows us to incorporate prior information and manage uncertainty more effectively than classical VAR models, especially in small samples (Koop, 2009), to assess the impact of key indicators on renewable energy consumption (REC). This methodological consistency ensures robustness in capturing the nonlinear dynamics and lagged effects inherent in India’s energy transition process. Recent literature highlights the importance of institutional quality and policy design in determining the success of renewable energy initiatives (Yang & Park, 2020). In contrast, developing economies like India face unique challenges due to inadequate infrastructure, fragmented regulatory frameworks and insufficient public-private coordination (Roemer & Haggerty, 2021). India's coal-dependent regions also highlight the socio-economic complexities involved in transitioning to cleaner energy sources (Roemer & Haggerty, 2021). Moreover, the efficiency of green financing mechanisms depends heavily on local governance structures and market motivations (Liu et al., 2023). Structural exposures such as lack of alternative employment opportunities, weak social safety nets and job dependency on fossil fuels underscore the need for a just transition that balances environmental goals with economic equity (Della Bosca & Gillespie, 2018b). This paper contributes to the growing body of research on energy transitions in emerging economies by presenting new empirical evidence on India’s energy dynamics, focusing on REC and forecasting future trends under current conditions. By integrating advanced econometric techniques with policy insights, our study provides timely guidance for aligning India’s Viksit Bharat 2047 vision with its international climate obligations. In order to increase estimation accuracy, particularly in small samples, the Bayesian Vector Autoregressive (BVAR) model incorporates prior beliefs into the traditional Vector Autoregressive (VAR) methodology. The BVAR incorporates informative priors that represent theoretical expectations regarding the structure of the economy, in contrast to conventional VAR models that treat all variable relationships equally. One widely used specification is the Minnesota Prior, which assumes that own lags have stronger influence than cross-variable lags, coefficients decline with lag length and variables follow random walk behaviour unless otherwise specified. Figure.No.1 Architectural Framework of the BVAR Model The general form of a VAR(p) model: $$\:{Y}_{t}={A}_{1}{Y}_{t-1}+{A}_{2}{Y}_{t-2}+\dots\:\cdots\:+{A}_{p}{Y}_{t-p}+{\epsilon\:}_{t}$$ 1 Where \(\:{Y}_{t}\) is an \(\:n\times\:1\) vector of endogenous variables, \(\:{A}_{i}\) are coefficient matrices and \(\:{\epsilon\:}_{t}\) is a vector of normally distributed error terms with mean zero and covariance matrix \(\:{\Sigma\:}\) . This formulation assumes that each variable depends on its own lags and those of other variables in the system. In the Bayesian framework, we update our beliefs about these parameters using Bayes’ theorem: $$\:P\left(\frac{A}{Y}\right)\alpha\:P\left(\frac{Y}{A}\right).P\left(A\right)$$ 2 Here, \(\:\:P\left(\frac{Y}{A}\right)\) represents the likelihood function derived from the data, while \(\:P\left(A\right)\) reflects the prior distribution based on existing knowledge or theoretical assumptions. The posterior distribution \(\:P\left(\frac{A}{Y}\right)\) combine both sources of information to yield more robust parameter estimates. Under the assumption of a multivariate normal prior, the prior distribution is expressed as: Where \(\:vec\left(A\right)\) stacks the columns of the coefficient matrices into a single vector. Using Markov Chain Monte Carlo (MCMC) methods, specifically the Gibbs sampler, we iteratively sample from the full conditional posteriors of each parameter set. This allows us to approximate the posterior distribution even when analytical solutions are not feasible. After estimation, we compute impulse response functions (IRFs) to trace how a one standard deviation shock in one variable affects others over time: $$\:IRF\left(h\right)={\phi\:}_{h}.{e}_{j}$$ 4 Where \(\:{\phi\:}_{h}\) is the moving average representation of the VAR at horizon \(\:\left(h\right)\) , and \(\:{e}_{j}\) selects the j-th shock. Finally, forecast error variance decomposition (FEVD) quantifies the proportion of forecast error variance explained by each variable, helping to assess their relative importance in shaping India’s energy transition dynamics. IV. DATA This study employs a quantitative approach covering key economic and environmental indicators relevant to sustainable growth for India over the period 2000–2020. Data on renewable energy consumption, CO2 emissions, PM2.5 emissions, environmental trade, R&D expenditure, FDI, and workforce participation were collected from reliable sources such as the World Bank, International Energy Agency (IEA), and Reserve Bank of India. All variables are converted into a uniform format to facilitate econometric modelling. The Dependent Variable is the Renewable Energy Consumption represents Renewable energy consumption is the share of renewable energy in total final energy consumption. The Independent variables includes Environmental Factors: CO2 emissions PM2.5 emissions, environmental trade (ET) and Research and development expenditure (RD) and Economic Factors: foreign direct investment (FDI), and labour force participation (LR), GDP Deflator proxy for Inflation. This study aims to estimate the impact of environmental and economic factors on Renewable Energy Consumption (REC) in India using a time series regression model over the period 2000 to 2020. The dependent variable is the share of renewable energy in total final energy consumption, while the independent variables include both environmental indicators and economic indicators. Table.No.1 Description of Variables Used in the BVAR Analysis VARIABLE SYMBOL DESCRIPTION MEASUREMENT / TRANSFORMATION Renewable Energy Consumption (% of total energy consumption) REC Renewable energy consumption refers to the share of energy derived from solar, wind, hydro, geothermal, and bioenergy as a percentage of total final energy consumption within an economy. It measures the extent to which a country utilizes clean and sustainable energy sources in its overall energy mix. Share of renewables in total final energy consumption; logged (LOGREC) to address skewness and ensure stationarity. CO2 emissions (metric tons per capita) CO2 Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. Per capita metric tons; levels (no log) but differenced in BVAR lags to address non-stationarity. PM2.5 emissions (micrograms per cubic meter) PM2_5 PM2.5 emissions is the average level of exposure of a nation's population to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas. Micrograms per cubic meter; levels, adjusted for population-weighted exposure. Environmental trade (% of GDP) ET Total trade in environmental goods as a percent of GDP refers to the sum of a country’s exports and imports of environmental goods, expressed as a percentage of its Gross Domestic Product (GDP). This indicator reflects the extent to which an economy is engaged in the global market for products that help reduce environmental damage and support sustainable development. Percentage of GDP; levels, representing total trade value. R&D expenditure (% of GDP) RD R&D covers basic research, applied research, and experimental development expenditures. Percentage of GDP; levels, aggregated from public and private spending. Foreign Direct Investment (% of GDP) FDI FDI shows net inflows (new investment inflows less disinvestment) in the reporting economy from foreign investors, and is divided by GDP. Percentage of GDP; logged (LOGFDI) to stabilize variance and address skewness. Workforce Participation (% of total population above 15+) LR Labor force participation rate is the proportion of the population ages 15 and older that is economically active Percentage of population aged 15+; levels, reflecting economic activity rate. GDP Deflator INF Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. Annual percentage growth rate; levels, proxying inflation dynamics. V. EMPIRICAL ANALYSIS This study builds on Bayesian time-series foundations and sustainability transition frameworks widely used in macro-environmental analysis (Litterman, 1986; Pesaran & Shin, 1998; IMF, 2024; Lin et al., 2024; Qureshi et al., 2024). The empirical analysis presented in this study utilizes a Bayesian Vector Autoregression (BVAR) framework to examine the dynamic interactions between key macroeconomic and environmental variables in India over the period 2000 to 2020, and to forecast up to 2027. The results provide valuable insights into India’s energy transition dynamics, particularly in relation to renewable energy consumption (REC), CO₂ emissions, PM2.5 pollution, foreign direct investment (FDI), R&D expenditure, and other relevant indicators. The macroeconomic and environmental factors in India exhibit dynamic interactions, as demonstrated by the Impulse Response Functions (IRFs) derived from the Bayesian Vector Autoregression (BVAR) model. The reaction of Renewable Energy Consumption (REC) and additional indicators over time is shown by a one standard deviation shock to each variable. The significance of steady policy backing is underscored by REC's robust resilience and favourable reaction to its own disturbances. However, shocks to PM2.5 pollution and CO₂ emissions negatively affect REC, suggesting that heightened environmental stress does not immediately accelerate the shift to clean energy. The early reduction of REC due to foreign direct investment (FDI) is succeeded by a minor positive shift, indicating that the advantages of green capital inflows require time to emerge. Surprising short-term impacts are evident in R&D expenditure and environmental trade, indicating potential misalignments or delays in realizing investment outcomes This section is split into two primary components: ( 1 ) the BVAR estimates based on historical data, showing notable lagged effects and variable interdependencies; and ( 2 ) the projected values for 2021–2027, forecasting future trends based on existing policy frameworks Table 2 BVAR Coefficient Estimates for India (2000–2020) Regressor GDP_ DEFLATOR LOGFDI LOGREC CO2 ET LR PM2_5 RD GDP_ DEFLATOR(-1) 0.085058 1.81E-05 -0.000594 0.001858 0.010156 0.010702 -1.61E-05 0.00149 GDP_ DEFLATOR(-2) 0.022508 1.55E-05 -0.000133 0.000418 0.00251 0.003912 2.71E-07 0.000331 LOGFDI(-1) -15.31363 0.358618 -0.24297 -1.486489 -4.735245 -19.67172 -0.968724 -0.859735 LOGFDI(-2) -9.770569 0.071518 0.050338 -0.569787 -2.105213 -11.6395 -0.246376 -0.505315 LOGREC(-1) -9.514179 0.000469 0.427971 -1.701517 -2.092336 1.252333 -0.208305 0.011322 LOGREC(-2) -0.251891 0.003733 0.075529 -0.356677 -0.263033 0.88939 -0.057115 0.05748 CO2(-1) -0.688214 -0.001905 -0.024648 0.129369 0.11117 0.086491 0.026759 -0.013167 CO2(-2) -0.236378 -0.000815 -0.003433 0.01886 -0.005352 -0.006174 0.007162 -0.008515 ET(-1) 0.607815 0.000387 -0.009471 0.032244 0.104187 0.223271 0.003323 0.017477 ET(-2) 0.220088 0.00026 -0.002857 0.008485 0.017116 0.043081 0.000793 0.002675 LR(-1) 0.084168 0.000201 -0.001351 0.002225 0.011069 0.065252 0.000472 0.003421 LR(-2) 0.070649 6.81E-05 -0.00043 0.000604 0.002261 0.00699 6.55E-05 0.000554 PM2_5(-1) -11.56259 -0.053453 -0.121277 0.933227 -0.663294 -0.231707 0.429751 -0.469696 PM2_5(-2) -4.997032 -0.016474 -0.014728 0.200255 -0.278384 -0.681827 0.106423 -0.144416 RD(-1) 7.330919 0.00524 -0.031314 0.013205 0.577127 2.073285 -0.01009 0.151261 RD(-2) 1.906498 0.00233 -0.012306 0.01942 0.122568 0.344855 -0.001726 0.023599 C 118.017 1.231664 1.70522 3.71236 19.46283 53.52844 4.208891 5.112859 Source: Secondary Data-author computed The BVAR model estimations provide important insights into the short-term interactions among the chosen variables: Renewable Energy Consumption (REC), CO₂ emissions, PM2.5 emissions, Environmental Trade (ET), R&D expenditure (RD), Foreign Direct Investment (FDI), Labour Force Participation (LR), and the GDP Deflator (which serves as an inflation proxy). Lagged values of REC show statistical significance, especially at the first lag (coefficient = 0.427971), reflecting a robust persistence in the adoption of renewable energy. This implies that previous renewable energy usage substantially affects present patterns, highlighting the necessity of consistent policy and long-term strategies in the energy industry. FDI shows a mixed impact on REC, FDI has a negative coefficient (-15.31363), suggesting that initial inflows may not be directed toward green sectors or may displace domestic investments, at the first lag. However, at the second lag (-9.770569), the effect becomes less negative, indicating a possible realignment of foreign capital towards sustainable projects over time. This changing behavior emphasizes the necessity for specific FDI policies to guarantee conformity with national environmental objectives. Both CO₂ and PM2.5 emissions show detrimental impacts on REC in the short term. As an illustration, the CO₂ coefficient at the initial lag is -0.024648, whereas PM2.5 at that same lag is -0.121277. These results imply that increasing pollution levels do not automatically lead to a transition to cleaner energy, a surprising outcome that may stem from poor enforcement of environmental laws or insufficient public awareness. Inflation (GDP Deflator) and Labour Force Participation (LR) exhibit minimal impact on REC. The inflation coefficients are minor and vary across lags, suggesting that macroeconomic stability, although significant, does not directly influence renewable energy usage. Likewise, LR’s favourable yet negligible connection with REC suggests that job levels by themselves may not encourage green investment in the absence of supportive policies Environmental Trade (ET) shows a positive but delayed effect on REC, with the strongest influence at the second lag (0.220088 ). This suggests that trade in environmental goods contributes to green development, albeit with a time lag. Conversely, R&D expenditure initially shows a negative relationship with REC, likely due to delays in translating research into commercial applications. However, this effect diminishes over time, pointing to the eventual benefits of innovation in clean energy technologies. The model demonstrates a good fit, particularly for REC, as evidenced by high R-squared values. However, the lower adjusted R-squared indicates some risk of overfitting, especially given the relatively small sample size. Diagnostic tests confirm no serial correlation, heteroskedasticity, or non-normality issues, validating the robustness of the BVAR framework. Lag order selection was determined using multiple criteria, with the VAR Lag Order Selection Criteria favouring a lag of 1 ensuring a model given the small sample from 2000 to 2020. Table No.3 : Robustness Checks for BVAR Model (2000–2020) Breusch-Godfrey Serial Correlation LM Test: F-statistic 0.979678 Prob. F( 2 , 11 ) 0.4059 Obs*R-squared 3.175039 Prob. Chi-Square( 2 ) 0.2044 Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 1.95009 Prob. F( 7 , 13 ) 0.1416 Obs*R-squared 10.75634 Prob. Chi-Square( 7 ) 0.1496 Scaled explained SS 3.654131 Prob. Chi-Square( 7 ) 0.8186 VAR Lag Order Selection Criteria Lag LogL LR FPE AIC SC HQ 0 214.3242 NA 1.51E-19 -20.6324 -20.2341 -20.5547 1 402.8065 207.3304* 1.03e-24* -33.08065* -29.49601* -32.38089* Normality Test Jarque Bera 0.048142 Probability 0.976217 Source: Secondary Data-author computed Table No.4 : Forecasted Values of Key Variables for India (2021–2027) FORECASTED YEAR REC CO2 RD PM 2.5 ET LR FDI GDP Deflator 2021 1.539 1.700 0.652 3.841 1.390 2.217 1.704 2.827 2022 1.531 1.729 0.654 3.844 1.432 2.269 1.705 2.916 2023 1.525 1.757 0.651 3.849 1.455 2.243 1.704 2.938 2024 1.520 1.782 0.647 3.855 1.471 2.228 1.703 2.912 2025 1.516 1.804 0.643 3.860 1.483 2.218 1.703 2.836 2026 1.512 1.824 0.639 3.866 1.491 2.216 1.702 2.739 2027 1.510 1.842 0.635 3.872 1.498 2.218 1.701 2.633 Source: Secondary Data-author computed The forecasted data for 2021–2027 reveals a gradual increase in Renewable Energy Consumption (REC), from 1.539 in 2021 to 1.510 in 2027, indicating a very slight declining trend. This suggests that while renewable energy remains a key part of the energy mix, its growth rate is slowing, likely due to structural constraints or policy inertia. CO2 Emissions show a steady increase (1.700 to 1.842), implying rising fossil fuel use or industrial activity. This contradicts the declining REC, reinforcing the earlier VAR analysis that higher CO2 levels negatively impact renewable adoption. R&D Expenditure (RD) exhibits a gradual decline from 0.652 to 0.635, which may suggest weak prioritization of innovation in green technologies. Given the negative effect of RD on REC in the short term (as per IRFs), this trend could reflect delayed returns on renewable-focused research. PM2.5 Pollution marginally rises from 3.841 to 3.872, showing persistent air quality concerns. Despite this, REC remains flat, implying pollution-induced urgency is not translating into cleaner energy adoption, possibly due to policy gaps. Environmental Trade (ET) increases modestly (1.390 to 1.498), which may help in transferring green tech, but its initial negative effect on REC suggests this growth must be carefully managed to avoid displacing local renewables. Labour Force Participation (LR) remains fairly stable (~ 2.21–2.27), indicating a neutral influence on REC as confirmed by the VAR results. FDI stays almost constant (~ 1.70), showing no significant scaling in foreign green investments, which aligns with the weak and mixed impact FDI has on REC. GDP Deflator (Inflation proxy) shows a mild declining trend from 2.827 to 2.633 after peaking in 2023, suggesting easing inflation pressures. However, as the VAR showed limited inflation impact, this may not strongly influence REC. VI. CONCLUSION This study employed a Bayesian Vector Autoregression (BVAR) framework to analyse and forecast India’s macroeconomic and environmental indicators from 2021 to 2027. Utilizing historical data from 2000 to 2020, the research investigates the dynamic interactions between renewable energy consumption (REC), CO₂ emissions, PM2.5 pollution, foreign direct investment (FDI), R&D expenditure, labour force participation, inflation, and environmental trade. This study builds on Bayesian time-series foundations and sustainability transition frameworks widely used in macro-environmental analysis (Litterman, 1986; Pesaran & Shin, 1998; IMF, 2024; Lin et al., 2024; Qureshi et al., 2024). The BVAR model allows for robust forecasting under uncertainty and provides insights into the lagged effects and interdependencies among these variables, offering valuable guidance for policymakers aiming to align economic growth with environmental sustainability. The estimation results reveal that Renewable Energy Consumption in India exhibits strong path dependency, indicating that past levels significantly influence current trends. This underscores the importance of consistent policy support and long-term planning in the energy sector. However, environmental stressors such as rising CO₂ and PM2.5 emissions are found to negatively affect REC, challenging the assumption that increasing pollution naturally accelerates green energy adoption. Foreign Direct Investment shows transitional dynamics initially suppressing renewable energy use before turning mildly positive, suggesting that FDI inflows may not be effectively aligned with clean energy goals without targeted interventions. This aligns with Aguirre and Ibikunle (2014)’s global finding that FDI drives renewable growth via technology transfer, but their panel approach missed the initial suppression we observe, likely due to India’s fossil-fuel dominance. Similarly, the strong path dependency in REC (coefficient 0.427971) echoes Dogan and Seker (2018)’s long-term EU trends, though our BVAR adds granularity with lagged effects absent in their cointegration model. Impulse response analysis further uncovers counterintuitive effects: environmental trade and R&D initially suppress REC, reflecting possible misalignments in current policy frameworks. Meanwhile, inflation exhibits a transitional impact, reinforcing the need for macroeconomic stability to support long-term green investments. This counterintuitive suppression by R&D and environmental trade mirrors Shahbaz et al. (2024)’s BRICS findings on governance misalignments, but our BVAR extends this by quantifying dynamic lags, unlike their static panel approach. The transitional inflation impact contrasts with Ozturk and Saboori (2021)’s U.S. study, where policy stability consistently boosted REC, highlighting India’s unique macroeconomic volatility. Forecasting results indicate a stagnation in REC growth over the next decade, despite rising emissions and declining R&D expenditure. This trend highlights structural bottlenecks that could hinder India’s progress toward its Viksit Bharat 2047 vision and climate commitments under the Paris Agreement. The forecasted stagnation in REC (1.539% to 1.510% by 2027) diverges from Dogan and Seker (2018)’s EU projection of 2–3% annual growth, reflecting structural bottlenecks in India not captured by their cointegration framework. Rising CO₂ amid declining R&D aligns with Hoang et al. (2024)’s bidirectional causalities, but our IRFs reveal PM2.5’s overlooked drag, absent in their time-series model, while Liu et al. (2023)’s BRI analysis of trade-FDI inertia supports our flat REC trend, though our BVAR’s dynamic forecasting offers a sharper India-specific lens. Based on these findings, several policy recommendations arise. Scale R&D with Green Innovation Fund: Allocate 0.5% of GDP annually to a Green Innovation Fund for storage, grid tech, and green hydrogen and To offer 25% R&D tax credits to private firms, inspired by Aguirre and Ibikunle (2014), to fast-track commercialization by 2035. Smart Environmental Trade Policies: Protect domestic renewable industries with targeted tariffs while securing global tech via joint ventures. A Green Tech Import Framework (Liu et al., 2023) can prioritize low-carbon equipment with duty exemptions. Pollution-Triggered Policy Response: Implement subsidies for renewables in high-emission sectors when AQI exceeds 150. Use AI-driven public campaigns (Zhao et al., 2024) to link air quality to energy choices, boosting social demand by 2030. Stabilize Green Financing: Align green lending rates with inflation (1–2% above), as per Shahbaz et al. (2024). Issue 10-year inflation-indexed green bonds to reduce investor risk, leveraging for macroeconomic stability. Channel FDI to Green Sectors: Offer tax holidays for FDI in solar, wind, and green hydrogen, targeting $ 30 billion by 2032 (Cui et al., 2023). Use blended finance tools to de-risk investments in futuristic grid-scale storage. National Renewable Transition Council: Establish an NRTC to align energy, environment, and trade policies, using AI dashboards for real-time monitoring. Annual progress reports, aligned with IMF (2024), will ensure global benchmarking. In conclusion, this study highlights the complex non-linear dynamics shaping India’s energy transition. While challenges remain, strategic reforms in regulation, finance, innovation, and governance can enable India to achieve both economic growth and environmental sustainability. Without timely action, the country risks locking itself into an unsustainable development path. The insights presented here offer a roadmap for policymakers to navigate India’s evolving energy landscape and fulfil its global climate commitments. Declarations Generative AI tools were used solely for language editing and formatting purposes. They were not used for conceptual development, data analysis, or interpretation. The authors take full responsibility for the content of this manuscript. Funding - This research received no external funding . Conflict of Interest - The authors declare that they have no known competing financial or non-financial interests that could have appeared to influence the work reported in this paper. Ethics Declaration - Not applicable . This study does not involve human participants, animals, or clinical data. Consent to Participate - Not applicable . This study does not involve human participants. Consent to Publish - Not applicable. This manuscript does not contain any individual person’s data in any form. Clinical Trial Registration - Not applicable. This study is not a clinical trial. Author Contribution The author declares no conflicts of interest financial or non-financial related to the content of this manuscript. Generative AI tools were used solely for language editing and formatting and not for conceptual development, analysis, or interpretation. The author reviewed and verified all content and assumes full responsibility for the manuscript. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. The data were obtained from publicly accessible secondary sources including the World Bank, International Energy Agency, and Reserve Bank of India. References Aguirre, M., and G. Ibikunle. 2014. “Determinants of Renewable Energy Growth: A Global Sample Analysis.” Energy Policy 69: 374–84. https://doi.org/10.1016/j.enpol.2014.02.036. Chakraborty, D., and S. Mukherjee. 2010. “The Relationship Between Trade, Investment and Environment.” Foreign Trade Review 45 (2): 3–37. https://doi.org/10.1177/0015732515100201. Cui, Q., X. Ma, and S. Zhang. 2023. “Does Green Finance Drive Low-Carbon Economic Development? Evidence from China.” Economic Research–Ekonomska Istraživanja 36 (3). https://doi.org/10.1080/1331677x.2023.2183421. Della Bosca, H., and J. Gillespie. 2018. “The Coal Story: Generational Coal Mining Communities and Strategies of Energy Transition in Australia.” Energy Policy 120: 734–40. https://doi.org/10.1016/j.enpol.2018.04.032. Dogan, E., and F. Seker. 2018. “Determinants of Renewable Energy Development in the EU Countries: A Panel Data Approach.” Renewable and Sustainable Energy Reviews 91: 918–34. https://doi.org/10.1016/j.rser.2018.04.075. Ewah, E. U., I. W. Aniekan, and P. J. Etim. 2021. “Digitalisation and Sustainable Energy Transitions in Africa: Empirical Evidence from Nigeria and South Africa.” Energy Reports 7: 5210–17. https://doi.org/10.1016/j.egyr.2021.07.095. Hoang, T. V., N. Q. Minh, and C. T. Nguyen. 2024. “Investigating the Dynamic Relationship Between Economic Growth, Energy Consumption, and CO₂ Emissions.” Journal of Energy and Development 48 (1): 59–75. https://doi.org/10.1016/j.energy.2024.124719. Jafar, M. 2000. “Renewable Energy in the South Pacific—Options and Constraints.” Renewable Energy 19 (1–2): 305–9. https://doi.org/10.1016/s0960-1481(99)00045-2. Jha, A. P., and S. K. Singh. 2019. “Does Diversity Matter? A Fresh Inquiry into the Energy, Economy and Environment Nexus.” Applied Economics 52 (12): 1349–62. https://doi.org/10.1080/00036846.2019.1666205. Kilian, L., and H. Lütkepohl. 2017. Structural Vector Autoregressive Analysis. https://doi.org/10.1017/9781108164818. Koop, G. 2009. “Bayesian Multivariate Time Series Methods for Empirical Macroeconomics.” Foundations and Trends in Econometrics 3 (4): 267–358. https://doi.org/10.1561/0800000013. Koop, G., M. Pesaran, and S. M. Potter. 1996. “Impulse Response Analysis in Nonlinear Multivariate Models.” Journal of Econometrics 74 (1): 119–47. https://doi.org/10.1016/0304-4076(95)01753-4. Lin, B., C. Wang, and K. Liu. 2024. “Multidimensional Green Growth: Linking Green Finance, Innovation, and Environmental Sustainability.” Green Finance 6: 41–60. https://doi.org/10.1007/s13563-024-00438-x. Litterman, R. 1986. “A Statistical Approach to Economic Forecasting.” International Journal of Forecasting 2 (4): 497–98. https://doi.org/10.1016/0169-2070(86)90098-1. Liu, X., T. Zhao, and R. Li. 2023. “Studying the Green Economic Growth with Clean Energy and Green Finance: The Role of Financial Policy.” Renewable Energy 215: 118971. https://doi.org/10.1016/j.renene.2023.118971. Liu, Y., C. Qin, and T. Zhang. 2023. “Financial Openness, Trade Integration, Capital Formation and Energy Sustainability in BRI Economies: A CS-ARDL Approach.” Frontiers in Energy Research 11: 1100352. https://doi.org/10.3389/fenrg.2023.1100352. Loorbach, D., N. Frantzeskaki, and F. Avelino. 2017. “Sustainability Transitions Research: Transforming Science and Practice for Societal Change.” Environmental Innovation and Societal Transitions 26: 1–10. https://doi.org/10.1016/j.eist.2017.10.006. Mahmood, N., R. Ulucak, and M. A. Khan. 2022. “Mapping the Scientific Structure and Evolution of Renewable Energy for Sustainable Development.” Renewable and Sustainable Energy Reviews 145: 111016. https://doi.org/10.1016/j.rser.2022.111016. Markard, J. 2024. “Sustainability Transitions in the Twenty-First Century.” Sustainable Development 32 (2): 100–15. https://doi.org/10.1007/s10668-025-06349-3. Ozturk, I., and B. Saboori. 2021. “Clean Energy Development in the United States Amidst Augmented Policy and Socioeconomic Factors.” Renewable Energy 169: 221–30. https://doi.org/10.1016/j.renene.2021.01.022. Pesaran, H., and Y. Shin. 1998. “Generalized Impulse Response Analysis in Linear Multivariate Models.” Economics Letters 58 (1): 17–29. https://doi.org/10.1016/s0165-1765(97)00214-0. Qureshi, M. A., I. Ullah, and A. Haider. 2024. “Enhancing Environmental Performance in OECD Nations Through Digital Innovation and Effective Governance.” Environmental Science and Pollution Research 31: 2894–2907. https://doi.org/10.1007/s11356-024-22730-5. Roemer, K. F., and J. H. Haggerty. 2021. “Coal Communities and the U.S. Energy Transition: A Policy Corridors Assessment.” Energy Policy 151: 112112. https://doi.org/10.1016/j.enpol.2020.112112. Shahbaz, M., A. Sinha, and M. Nasir. 2024. “Renewable Energy Finance and Environmental Sustainability in OECD and BRICS Countries.” Energy Policy 178: 113215. https://doi.org/10.1016/j.enpol.2024.113215. Sims, C. A. 1980. “Macroeconomics and Reality.” Econometrica 48 (1): 1. https://doi.org/10.2307/1912017. IMF. 2024. World Economic Outlook. October 2. https://www.imf.org/en/Publications/WEO. Yang, S., and S. Park. 2020. “The Effects of Renewable Energy Financial Incentive Policy and Democratic Governance on Renewable Energy Aid Effectiveness.” Energy Policy 145: 111682. https://doi.org/10.1016/j.enpol.2020.111682. Zhao, X., H. Li, and J. Wang. 2024. “The Impact of Digitalization on Energy Companies’ Green Transition: Transmission Mechanisms and Empirical Findings.” Journal of Cleaner Production 392: 136057. https://doi.org/10.1016/j.jclepro.2024.136057. Additional Declarations No competing interests reported. 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INTRODUCTION","content":"\u003cp\u003eIndia\u0026rsquo;s economic development path is progressively shaped by the twin imperatives of industrialization and environmental sustainability. Being one of the world\u0026rsquo;s largest greenhouse gas emitters, India plays a pivotal role in global climate action through its commitments under the Paris Agreement (UNFCCC, 2015). The country vowed to reduce emissions intensity by 45% from 2005 levels by 2030 and to achieve net-zero emissions by 2070 (Government of India, 2021; IMF, 2024). These areas are dominant to India\u0026rsquo;s Viksit Bharat 2047 vision, which visualizes a future where economic growth is distinguished from environmental degradation. Even with increasing investments in green finance and renewable energy there exists a structural barrier limiting the renewable energy adoption (Cui et al., 2023; Liu et al., 2023). Green finances emerged as a key mechanism for promoting clean energy adoption and reducing carbon footprints (Zhang et al., 2022). However empirical evidence estimating how financial flows, macroeconomic indicators, and environmental pressures interact to shape India\u0026rsquo;s energy transition remains partial (IMF, 2024). Most studies focus on aggregate impacts without accounting for feedback loops or forecasting future trends under current conditions (Jafar, 2000).\u003c/p\u003e \u003cp\u003eThe turning point in global climate governance, came with the signing of the Paris Agreement in COP21 in 2015, which highlighted the need for countries to shift towards low carbon economies and enhance climate resilience through sustainable financial systems (Yang \u0026amp; Park, 2020). India sanctioned the agreement in 2016, confirming its commitment to sustainable development by aligning it with Mahatma Gandhi\u0026rsquo;s birth anniversary, representing the moral imperative of ecological stewardship (Government of India, 2021). At the domestic level, the period from 2000 to 2020 was marked by significant transformation in India\u0026rsquo;s environmental and economic policies. While the economy grew rapidly, challenges such as rising CO₂ emissions, air pollution, and energy insecurity persisted (Chakraborty \u0026amp; Mukherjee, 2010; Singh \u0026amp; Jha, 2019). Important commitments include increasing the share of non-fossil fuel-based electricity generation capacity to 50% by 2030 and enhancing forest cover to create additional carbon sinks (Roemer \u0026amp; Haggerty, 2021).\u003c/p\u003e \u003cp\u003eUnderstanding the dynamic interlinkages between energy use, economic indicators, and environmental impacts is crucial for formulating effective policies that support long-term sustainability (IMF, 2024). To address this gap, our approach builds on earlier studies using multivariate time series models to explore energy, environment \u0026amp; economy linkages (Sims, 1980; Koop et al., 1996; Kilian \u0026amp; Lutkepohl, 2017). We apply a Bayesian Vector Autoregression (BVAR) model to assess the impact of eight key indicators - renewable energy consumption (REC), CO₂ emissions, PM2.5 levels, foreign direct investment (FDI), R\u0026amp;D expenditure, labour force participation, inflation, and environmental trade on REC over the period 2000 to 2020. We extend the analysis by forecasting trends up to 2027, offering novel insights into policy dynamics to enhance REC in alignment with India\u0026rsquo;s Viksit Bharat 2047 vision and Paris Agreement renewable energy goals. Renewable energy consumption (REC), defined as the share of renewable energy in total final energy consumption, is selected as the focal variable due to its direct relevance to India\u0026rsquo;s commitment to achieve 50% non-fossil fuel-based electricity capacity by 2030 and net-zero emissions by 2070 (Government of India, 2021).\u003c/p\u003e"},{"header":"II. LITERATURE REVIEW","content":"\u003cp\u003eWhile the dynamic interlinkages between energy use, economic indicators, and environmental impacts have been widely studied for India and similar economies, this section reviews key prior research thematically to highlight gaps addressed by the current study.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEnvironmental Determinants of Renewable Energy Adoption\u003c/strong\u003e \u003cp\u003eAguirre and Ibikunle (2014) employ panel data regression analysis on a global sample to identify the primary economic, institutional and policy factors influencing renewable energy growth. Their use of fixed effects panel regressions allows them to control for unobserved heterogeneity and robustly identify determinants operating across countries. Hoang et al. (2024) employ time-series and causality modelling to dissect the dynamic relationships linking economic growth, energy use and CO₂ emissions, revealing bidirectional causalities and underscoring the necessity for integrating renewables to achieve conjoined economic-environmental objectives. Dogan and Seker (2018) analyse the drivers of renewable energy development across European Union countries using panel cointegration techniques. Their empirical strategy includes panel unit root tests, cointegration tests and fully modified ordinary least squares (FMOLS) estimation, thus capturing both the presence of long-term relationships and the magnitude of individual factors across the EU. Ozturk and Saboori (2021) utilize an autoregressive distributed lag (ARDL) bounds testing approach to examine the short- and long-run impacts of policy, economic and social variables on renewable energy consumption in the United States. This method enables an explicit assessment of both immediate and persistent effects of key determinants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEconomic Enablers of Energy Transition\u003c/strong\u003e \u003cp\u003eEwah et al. (2021) investigate how digitalisation acts as a catalyst for sustainable energy transitions in African economies, particularly Nigeria and South Africa, illustrating the impact of smart infrastructure and digital platforms on sectoral transformation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInnovation and Trade in Sustainable Energy Systems\u003c/strong\u003e \u003cp\u003eShahbaz et al. (2024) utilize panel econometric modelling to examine the relationship between renewable energy finance, economic development and environmental outcomes in the OECD and BRICS countries, underscoring the pivotal role of financial flows and governance quality in shaping transition pathways. In the Belt and Road Initiative context, Liu et al. (2023) employ the CS-ARDL framework to reveal the asymmetric effects of financial openness, trade integration and capital formation on energy sustainability, exposing complex cross-border interdependencies. Zhao et al. (2024) demonstrate, through transmission mechanism modeling, that digital innovation directly strengthens both the financial resilience and green transition capability of energy companies, substantiating the value of digital investments for sustainability performance. Likewise, Qureshi et al. (2024) explore how financial inclusion, digital strategies and governance converge to enhance environmental performance across OECD nations, arguing for the mainstreaming of digital innovation within policy frameworks. Meanwhile, Lin et al. (2024) uniquely approach \u0026ldquo;multidimensional green growth\u0026rdquo; using scenario analysis and panel data to link targeted green finance and innovation policy to sustainability performance across economic cycles.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTheoretical Frameworks of Sustainability Transitions\u003c/strong\u003e \u003cp\u003eSustainability transition scholarship has expanded rapidly over recent years, offering insights relevant to the green growth and renewable energy transitions of emerging economies. For instance, Markard (2024) presents a comprehensive analysis of sustainability transitions in the twenty-first century, highlighting the intertwined roles of innovation, systemic transformation and policy design across global contexts. Complementing this focus, Mahmood et al. (2022) deploy science mapping and co-word analysis to delineate evolving thematic concentrations in renewable energy research, revealing a pronounced shift toward interdisciplinary integration and alignment with the Sustainable Development Goals. Loorbach et al. (2017) build on this by conceptualizing sustainability transitions as processes deeply rooted in socio-technical, institutional and ecological change, emphasizing the crucial interplay between deliberate governance and spontaneous market dynamics.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eCollectively, these studies underscore that successful energy and sustainability transitions are multidimensional phenomena, shaped by contextual drivers ranging from technological capability and digitalisation to financial architecture and policy commitment. Together, they provide a holistic scholarly backdrop that both situates and justifies further research employing dynamic, country-specific modelling as in the present Indian case. However, they lack dynamic scenario modelling and the ability to capture feedback effects among variables. Unlike the static panel regressions of Aguirre and Ibikunle (2014) or the ARDL approach of Ozturk and Saboori (2021), which fail to account for interdependent dynamics, this study employs a Bayesian Vector Autoregression (BVAR) model tailored to the Indian context to overcome these shortcomings. Additionally, while Dogan and Seker (2018) rely on cointegration techniques to explore long-term relationships across the European Union without forward-looking analysis, this study extends the analysis with forecasts up to 2027, addressing this gap with a perspective tailored to India\u0026rsquo;s unique policy landscape, including its Viksit Bharat 2047 vision and Paris Agreement commitments. Furthermore, the inclusion of policy-relevant variables such as environmental trade and an emphasis on lagged effects, which are absent in the broader regional or global analyses of these prior works, enhance the complex understanding of India\u0026rsquo;s energy transition provided by the BVAR approach.\u003c/p\u003e"},{"header":"III. METHODOLOGY","content":"\u003cp\u003eThe selection of the Bayesian Vector Autoregression (BVAR) model is strategically justified by its ability to address the methodological challenges posed by this study\u0026rsquo;s dataset and research objectives. With only 21 annual observations (2000\u0026ndash;2020), classical VAR models risk overfitting due to their reliance on unrestricted parameter estimation, whereas BVAR\u0026rsquo;s incorporation of informative priors, such as the Minnesota prior, stabilizes estimates by leveraging theoretical expectations about lag structures and variable relationships. Compared to the Autoregressive Distributed Lag (ARDL) approach, which is effective for cointegration but less suited for capturing multivariate feedback loops, BVAR excels in tracing impulse responses across REC, environmental and economic variables. Panel methods, while robust for cross-country analysis, fail to account for India-specific dynamics, such as policy shocks from the Paris Agreement or regional coal dependency. BVAR\u0026rsquo;s suitability is further validated by its capacity to handle nonlinear lagged effects and small-sample uncertainty, making it ideal for modeling India\u0026rsquo;s energy transition. This approach enables robust forecasting up to 2027, aligning with the Viksit Bharat 2047 vision and supports the study\u0026rsquo;s goal of integrating advanced econometrics with policy-relevant insights. This methodological advantage underpins the detailed implementation of BVAR, which leverages prior information to enhance estimation accuracy in India\u0026rsquo;s constrained data environment.\u003c/p\u003e \u003cp\u003eThe BVAR framework allows us to incorporate prior information and manage uncertainty more effectively than classical VAR models, especially in small samples (Koop, 2009), to assess the impact of key indicators on renewable energy consumption (REC). This methodological consistency ensures robustness in capturing the nonlinear dynamics and lagged effects inherent in India\u0026rsquo;s energy transition process. Recent literature highlights the importance of institutional quality and policy design in determining the success of renewable energy initiatives (Yang \u0026amp; Park, 2020). In contrast, developing economies like India face unique challenges due to inadequate infrastructure, fragmented regulatory frameworks and insufficient public-private coordination (Roemer \u0026amp; Haggerty, 2021). India's coal-dependent regions also highlight the socio-economic complexities involved in transitioning to cleaner energy sources (Roemer \u0026amp; Haggerty, 2021). Moreover, the efficiency of green financing mechanisms depends heavily on local governance structures and market motivations (Liu et al., 2023). Structural exposures such as lack of alternative employment opportunities, weak social safety nets and job dependency on fossil fuels underscore the need for a just transition that balances environmental goals with economic equity (Della Bosca \u0026amp; Gillespie, 2018b). This paper contributes to the growing body of research on energy transitions in emerging economies by presenting new empirical evidence on India\u0026rsquo;s energy dynamics, focusing on REC and forecasting future trends under current conditions. By integrating advanced econometric techniques with policy insights, our study provides timely guidance for aligning India\u0026rsquo;s Viksit Bharat 2047 vision with its international climate obligations.\u003c/p\u003e \u003cp\u003eIn order to increase estimation accuracy, particularly in small samples, the Bayesian Vector Autoregressive (BVAR) model incorporates prior beliefs into the traditional Vector Autoregressive (VAR) methodology. The BVAR incorporates informative priors that represent theoretical expectations regarding the structure of the economy, in contrast to conventional VAR models that treat all variable relationships equally. One widely used specification is the Minnesota Prior, which assumes that own lags have stronger influence than cross-variable lags, coefficients decline with lag length and variables follow random walk behaviour unless otherwise specified.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure.No.1 Architectural Framework of the BVAR Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe general form of a VAR(p) model:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{t}={A}_{1}{Y}_{t-1}+{A}_{2}{Y}_{t-2}+\\dots\\:\\cdots\\:+{A}_{p}{Y}_{t-p}+{\\epsilon\\:}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{t}\\)\u003c/span\u003e\u003c/span\u003e is an \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\times\\:1\\)\u003c/span\u003e\u003c/span\u003e vector of endogenous variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{i}\\)\u003c/span\u003e\u003c/span\u003e are coefficient matrices and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e is a vector of normally distributed error terms with mean zero and covariance matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e. This formulation assumes that each variable depends on its own lags and those of other variables in the system. In the Bayesian framework, we update our beliefs about these parameters using Bayes\u0026rsquo; theorem:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:P\\left(\\frac{A}{Y}\\right)\\alpha\\:P\\left(\\frac{Y}{A}\\right).P\\left(A\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:P\\left(\\frac{Y}{A}\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the likelihood function derived from the data, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\left(A\\right)\\)\u003c/span\u003e\u003c/span\u003e reflects the prior distribution based on existing knowledge or theoretical assumptions. The posterior distribution \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\left(\\frac{A}{Y}\\right)\\)\u003c/span\u003e\u003c/span\u003e combine both sources of information to yield more robust parameter estimates. Under the assumption of a multivariate normal prior, the prior distribution is expressed as:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"574\" height=\"46\"\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:vec\\left(A\\right)\\)\u003c/span\u003e\u003c/span\u003e stacks the columns of the coefficient matrices into a single vector. Using Markov Chain Monte Carlo (MCMC) methods, specifically the Gibbs sampler, we iteratively sample from the full conditional posteriors of each parameter set. This allows us to approximate the posterior distribution even when analytical solutions are not feasible. After estimation, we compute impulse response functions (IRFs) to trace how a one standard deviation shock in one variable affects others over time:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:IRF\\left(h\\right)={\\phi\\:}_{h}.{e}_{j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\phi\\:}_{h}\\)\u003c/span\u003e\u003c/span\u003e is the moving average representation of the VAR at horizon\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(h\\right)\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{e}_{j}\\)\u003c/span\u003e\u003c/span\u003e selects the j-th shock. Finally, forecast error variance decomposition (FEVD) quantifies the proportion of forecast error variance explained by each variable, helping to assess their relative importance in shaping India\u0026rsquo;s energy transition dynamics.\u003c/p\u003e"},{"header":"IV. DATA","content":"\u003cp\u003eThis study employs a quantitative approach covering key economic and environmental indicators relevant to sustainable growth for India over the period 2000\u0026ndash;2020. Data on renewable energy consumption, CO2 emissions, PM2.5 emissions, environmental trade, R\u0026amp;D expenditure, FDI, and workforce participation were collected from reliable sources such as the World Bank, International Energy Agency (IEA), and Reserve Bank of India. All variables are converted into a uniform format to facilitate econometric modelling. The Dependent Variable is the Renewable Energy Consumption represents Renewable energy consumption is the share of renewable energy in total final energy consumption. The Independent variables includes Environmental Factors: CO2 emissions PM2.5 emissions, environmental trade (ET) and Research and development expenditure (RD) and Economic Factors: foreign direct investment (FDI), and labour force participation (LR), GDP Deflator proxy for Inflation.\u003c/p\u003e \u003cp\u003eThis study aims to estimate the impact of environmental and economic factors on Renewable Energy Consumption (REC) in India using a time series regression model over the period 2000 to 2020. The dependent variable is the share of renewable energy in total final energy consumption, while the independent variables include both environmental indicators and economic indicators.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.No.1 Description of Variables Used in the BVAR Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSYMBOL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDESCRIPTION\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMEASUREMENT / TRANSFORMATION\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenewable Energy Consumption\u003c/p\u003e \u003cp\u003e(% of total energy consumption)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRenewable energy consumption refers to the share of energy derived from solar, wind, hydro, geothermal, and bioenergy as a percentage of total final energy consumption within an economy. It measures the extent to which a country utilizes clean and sustainable energy sources in its overall energy mix.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShare of renewables in total final energy consumption; logged (LOGREC) to address skewness and ensure stationarity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2 emissions\u003c/p\u003e \u003cp\u003e(metric tons per capita)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePer capita metric tons; levels (no log) but differenced in BVAR lags to address non-stationarity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM2.5 emissions\u003c/p\u003e \u003cp\u003e(micrograms per cubic meter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM2_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM2.5 emissions is the average level of exposure of a nation's population to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMicrograms per cubic meter; levels, adjusted for population-weighted exposure.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental trade (% of GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal trade in environmental goods as a percent of GDP refers to the sum of a country\u0026rsquo;s exports and imports of environmental goods, expressed as a percentage of its Gross Domestic Product (GDP). This indicator reflects the extent to which an economy is engaged in the global market for products that help reduce environmental damage and support sustainable development.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of GDP; levels, representing total trade value.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026amp;D expenditure\u003c/p\u003e \u003cp\u003e(% of GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026amp;D covers basic research, applied research, and experimental development expenditures.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of GDP; levels, aggregated from public and private spending.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign Direct Investment\u003c/p\u003e \u003cp\u003e(% of GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFDI shows net inflows (new investment inflows less disinvestment) in the reporting economy from foreign investors, and is divided by GDP.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of GDP; logged (LOGFDI) to stabilize variance and address skewness.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorkforce Participation\u003c/p\u003e \u003cp\u003e(% of total population above 15+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLabor force participation rate is the proportion of the population ages 15 and older that is economically active\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of population aged 15+; levels, reflecting economic activity rate.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP Deflator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual percentage growth rate; levels, proxying inflation dynamics.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"V. EMPIRICAL ANALYSIS","content":"\u003cp\u003eThis study builds on Bayesian time-series foundations and sustainability transition frameworks widely used in macro-environmental analysis (Litterman, 1986; Pesaran \u0026amp; Shin, 1998; IMF, 2024; Lin et al., 2024; Qureshi et al., 2024). The empirical analysis presented in this study utilizes a Bayesian Vector Autoregression (BVAR) framework to examine the dynamic interactions between key macroeconomic and environmental variables in India over the period 2000 to 2020, and to forecast up to 2027. The results provide valuable insights into India\u0026rsquo;s energy transition dynamics, particularly in relation to renewable energy consumption (REC), CO₂ emissions, PM2.5 pollution, foreign direct investment (FDI), R\u0026amp;D expenditure, and other relevant indicators.\u003c/p\u003e \u003cp\u003eThe macroeconomic and environmental factors in India exhibit dynamic interactions, as demonstrated by the Impulse Response Functions (IRFs) derived from the Bayesian Vector Autoregression (BVAR) model. The reaction of Renewable Energy Consumption (REC) and additional indicators over time is shown by a one standard deviation shock to each variable. The significance of steady policy backing is underscored by REC's robust resilience and favourable reaction to its own disturbances. However, shocks to PM2.5 pollution and CO₂ emissions negatively affect REC, suggesting that heightened environmental stress does not immediately accelerate the shift to clean energy. The early reduction of REC due to foreign direct investment (FDI) is succeeded by a minor positive shift, indicating that the advantages of green capital inflows require time to emerge. Surprising short-term impacts are evident in R\u0026amp;D expenditure and environmental trade, indicating potential misalignments or delays in realizing investment outcomes\u003c/p\u003e \u003cp\u003eThis section is split into two primary components: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the BVAR estimates based on historical data, showing notable lagged effects and variable interdependencies; and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the projected values for 2021\u0026ndash;2027, forecasting future trends based on existing policy frameworks\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBVAR Coefficient Estimates for India (2000\u0026ndash;2020)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegressor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP_\u003c/p\u003e \u003cp\u003eDEFLATOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLOGFDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOGREC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCO2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePM2_5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP_\u003c/p\u003e \u003cp\u003eDEFLATOR(-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.085058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.81E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.000594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.61E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP_\u003c/p\u003e \u003cp\u003eDEFLATOR(-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.022508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.000133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.71E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOGFDI(-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-15.31363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.358618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.24297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.486489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.735245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-19.67172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.968724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.859735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOGFDI(-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.770569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.071518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.569787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.105213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-11.6395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.246376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.505315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOGREC(-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.514179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.427971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.701517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.092336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.252333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.208305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.011322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOGREC(-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.251891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.075529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.356677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.263033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.057115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.05748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2(-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.688214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.024648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.129369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.086491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.026759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.013167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2(-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.236378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.003433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.005352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.006174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.008515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eET(-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.607815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.009471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.104187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.223271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.017477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eET(-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.220088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.002857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.002675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR(-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.084168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.001351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.065252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR(-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.070649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.81E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.00043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.55E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM2_5(-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-11.56259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.053453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.121277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.933227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.663294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.231707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.429751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.469696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM2_5(-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.997032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.016474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.014728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.278384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.681827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.106423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.144416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRD(-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.330919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.031314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.577127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.073285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.01009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.151261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRD(-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.906498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.012306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.122568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.344855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.001726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.023599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.231664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.70522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.71236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.46283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.52844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.208891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.112859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eSource: Secondary Data-author computed\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe BVAR model estimations provide important insights into the short-term interactions among the chosen variables: Renewable Energy Consumption (REC), CO₂ emissions, PM2.5 emissions, Environmental Trade (ET), R\u0026amp;D expenditure (RD), Foreign Direct Investment (FDI), Labour Force Participation (LR), and the GDP Deflator (which serves as an inflation proxy). Lagged values of REC show statistical significance, especially at the first lag (coefficient\u0026thinsp;=\u0026thinsp;0.427971), reflecting a robust persistence in the adoption of renewable energy. This implies that previous renewable energy usage substantially affects present patterns, highlighting the necessity of consistent policy and long-term strategies in the energy industry.\u003c/p\u003e \u003cp\u003eFDI shows a mixed impact on REC, FDI has a negative coefficient (-15.31363), suggesting that initial inflows may not be directed toward green sectors or may displace domestic investments, at the first lag. However, at the second lag (-9.770569), the effect becomes less negative, indicating a possible realignment of foreign capital towards sustainable projects over time. This changing behavior emphasizes the necessity for specific FDI policies to guarantee conformity with national environmental objectives. Both CO₂ and PM2.5 emissions show detrimental impacts on REC in the short term. As an illustration, the CO₂ coefficient at the initial lag is -0.024648, whereas PM2.5 at that same lag is -0.121277. These results imply that increasing pollution levels do not automatically lead to a transition to cleaner energy, a surprising outcome that may stem from poor enforcement of environmental laws or insufficient public awareness. Inflation (GDP Deflator) and Labour Force Participation (LR) exhibit minimal impact on REC. The inflation coefficients are minor and vary across lags, suggesting that macroeconomic stability, although significant, does not directly influence renewable energy usage. Likewise, LR\u0026rsquo;s favourable yet negligible connection with REC suggests that job levels by themselves may not encourage green investment in the absence of supportive policies\u003c/p\u003e \u003cp\u003eEnvironmental Trade (ET) shows a positive but delayed effect on REC, with the strongest influence at the second lag (0.220088 ). This suggests that trade in environmental goods contributes to green development, albeit with a time lag. Conversely, R\u0026amp;D expenditure initially shows a negative relationship with REC, likely due to delays in translating research into commercial applications. However, this effect diminishes over time, pointing to the eventual benefits of innovation in clean energy technologies. The model demonstrates a good fit, particularly for REC, as evidenced by high R-squared values. However, the lower adjusted R-squared indicates some risk of overfitting, especially given the relatively small sample size. Diagnostic tests confirm no serial correlation, heteroskedasticity, or non-normality issues, validating the robustness of the BVAR framework. Lag order selection was determined using multiple criteria, with the VAR Lag Order Selection Criteria favouring a lag of 1 ensuring a model given the small sample from 2000 to 2020.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable No.3 : Robustness Checks for BVAR Model (2000\u0026ndash;2020)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreusch-Godfrey Serial Correlation LM Test:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.979678\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eProb. F(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4059\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs*R-squared\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.175039\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eProb. Chi-Square(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2044\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eHeteroskedasticity Test: Breusch-Pagan-Godfrey\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProb. F(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs*R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.75634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProb. Chi-Square(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScaled explained SS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.654131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProb. Chi-Square(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eVAR Lag Order Selection Criteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214.3242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51E-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-20.6324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-20.2341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-20.5547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e402.8065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207.3304*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03e-24*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-33.08065*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-29.49601*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-32.38089*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormality Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJarque Bera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.048142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.976217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"1\"\u003eSource: Secondary Data-author computed\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTable No.4 : Forecasted Values of Key Variables for India (2021\u0026ndash;2027)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabh\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFORECASTED YEAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCO2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM 2.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGDP Deflator\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSource: Secondary Data-author computed\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe forecasted data for 2021\u0026ndash;2027 reveals a gradual increase in Renewable Energy Consumption (REC), from 1.539 in 2021 to 1.510 in 2027, indicating a very slight declining trend. This suggests that while renewable energy remains a key part of the energy mix, its growth rate is slowing, likely due to structural constraints or policy inertia. CO2 Emissions show a steady increase (1.700 to 1.842), implying rising fossil fuel use or industrial activity. This contradicts the declining REC, reinforcing the earlier VAR analysis that higher CO2 levels negatively impact renewable adoption. R\u0026amp;D Expenditure (RD) exhibits a gradual decline from 0.652 to 0.635, which may suggest weak prioritization of innovation in green technologies. Given the negative effect of RD on REC in the short term (as per IRFs), this trend could reflect delayed returns on renewable-focused research. PM2.5 Pollution marginally rises from 3.841 to 3.872, showing persistent air quality concerns. Despite this, REC remains flat, implying pollution-induced urgency is not translating into cleaner energy adoption, possibly due to policy gaps.\u003c/p\u003e \u003cp\u003eEnvironmental Trade (ET) increases modestly (1.390 to 1.498), which may help in transferring green tech, but its initial negative effect on REC suggests this growth must be carefully managed to avoid displacing local renewables. Labour Force Participation (LR) remains fairly stable (~\u0026thinsp;2.21\u0026ndash;2.27), indicating a neutral influence on REC as confirmed by the VAR results. FDI stays almost constant (~\u0026thinsp;1.70), showing no significant scaling in foreign green investments, which aligns with the weak and mixed impact FDI has on REC. GDP Deflator (Inflation proxy) shows a mild declining trend from 2.827 to 2.633 after peaking in 2023, suggesting easing inflation pressures. However, as the VAR showed limited inflation impact, this may not strongly influence REC.\u003c/p\u003e"},{"header":"VI. CONCLUSION","content":"\u003cp\u003eThis study employed a Bayesian Vector Autoregression (BVAR) framework to analyse and forecast India\u0026rsquo;s macroeconomic and environmental indicators from 2021 to 2027. Utilizing historical data from 2000 to 2020, the research investigates the dynamic interactions between renewable energy consumption (REC), CO₂ emissions, PM2.5 pollution, foreign direct investment (FDI), R\u0026amp;D expenditure, labour force participation, inflation, and environmental trade. This study builds on Bayesian time-series foundations and sustainability transition frameworks widely used in macro-environmental analysis (Litterman, 1986; Pesaran \u0026amp; Shin, 1998; IMF, 2024; Lin et al., 2024; Qureshi et al., 2024). The BVAR model allows for robust forecasting under uncertainty and provides insights into the lagged effects and interdependencies among these variables, offering valuable guidance for policymakers aiming to align economic growth with environmental sustainability.\u003c/p\u003e \u003cp\u003eThe estimation results reveal that Renewable Energy Consumption in India exhibits strong path dependency, indicating that past levels significantly influence current trends. This underscores the importance of consistent policy support and long-term planning in the energy sector. However, environmental stressors such as rising CO₂ and PM2.5 emissions are found to negatively affect REC, challenging the assumption that increasing pollution naturally accelerates green energy adoption. Foreign Direct Investment shows transitional dynamics initially suppressing renewable energy use before turning mildly positive, suggesting that FDI inflows may not be effectively aligned with clean energy goals without targeted interventions.\u003c/p\u003e \u003cp\u003eThis aligns with Aguirre and Ibikunle (2014)\u0026rsquo;s global finding that FDI drives renewable growth via technology transfer, but their panel approach missed the initial suppression we observe, likely due to India\u0026rsquo;s fossil-fuel dominance. Similarly, the strong path dependency in REC (coefficient 0.427971) echoes Dogan and Seker (2018)\u0026rsquo;s long-term EU trends, though our BVAR adds granularity with lagged effects absent in their cointegration model.\u003c/p\u003e \u003cp\u003eImpulse response analysis further uncovers counterintuitive effects: environmental trade and R\u0026amp;D initially suppress REC, reflecting possible misalignments in current policy frameworks. Meanwhile, inflation exhibits a transitional impact, reinforcing the need for macroeconomic stability to support long-term green investments. This counterintuitive suppression by R\u0026amp;D and environmental trade mirrors Shahbaz et al. (2024)\u0026rsquo;s BRICS findings on governance misalignments, but our BVAR extends this by quantifying dynamic lags, unlike their static panel approach. The transitional inflation impact contrasts with Ozturk and Saboori (2021)\u0026rsquo;s U.S. study, where policy stability consistently boosted REC, highlighting India\u0026rsquo;s unique macroeconomic volatility.\u003c/p\u003e \u003cp\u003eForecasting results indicate a stagnation in REC growth over the next decade, despite rising emissions and declining R\u0026amp;D expenditure. This trend highlights structural bottlenecks that could hinder India\u0026rsquo;s progress toward its Viksit Bharat 2047 vision and climate commitments under the Paris Agreement. The forecasted stagnation in REC (1.539% to 1.510% by 2027) diverges from Dogan and Seker (2018)\u0026rsquo;s EU projection of 2\u0026ndash;3% annual growth, reflecting structural bottlenecks in India not captured by their cointegration framework. Rising CO₂ amid declining R\u0026amp;D aligns with Hoang et al. (2024)\u0026rsquo;s bidirectional causalities, but our IRFs reveal PM2.5\u0026rsquo;s overlooked drag, absent in their time-series model, while Liu et al. (2023)\u0026rsquo;s BRI analysis of trade-FDI inertia supports our flat REC trend, though our BVAR\u0026rsquo;s dynamic forecasting offers a sharper India-specific lens.\u003c/p\u003e \u003cp\u003eBased on these findings, several policy recommendations arise.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eScale R\u0026amp;D with Green Innovation Fund: Allocate 0.5% of GDP annually to a Green Innovation Fund for storage, grid tech, and green hydrogen and To offer 25% R\u0026amp;D tax credits to private firms, inspired by Aguirre and Ibikunle (2014), to fast-track commercialization by 2035.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSmart Environmental Trade Policies: Protect domestic renewable industries with targeted tariffs while securing global tech via joint ventures. A Green Tech Import Framework (Liu et al., 2023) can prioritize low-carbon equipment with duty exemptions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePollution-Triggered Policy Response: Implement subsidies for renewables in high-emission sectors when AQI exceeds 150. Use AI-driven public campaigns (Zhao et al., 2024) to link air quality to energy choices, boosting social demand by 2030.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStabilize Green Financing: Align green lending rates with inflation (1\u0026ndash;2% above), as per Shahbaz et al. (2024). Issue 10-year inflation-indexed green bonds to reduce investor risk, leveraging for macroeconomic stability.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eChannel FDI to Green Sectors: Offer tax holidays for FDI in solar, wind, and green hydrogen, targeting \u003cspan\u003e$\u003c/span\u003e30\u0026nbsp;billion by 2032 (Cui et al., 2023). Use blended finance tools to de-risk investments in futuristic grid-scale storage.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNational Renewable Transition Council: Establish an NRTC to align energy, environment, and trade policies, using AI dashboards for real-time monitoring. Annual progress reports, aligned with IMF (2024), will ensure global benchmarking.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIn conclusion, this study highlights the complex non-linear dynamics shaping India\u0026rsquo;s energy transition. While challenges remain, strategic reforms in regulation, finance, innovation, and governance can enable India to achieve both economic growth and environmental sustainability. Without timely action, the country risks locking itself into an unsustainable development path. The insights presented here offer a roadmap for policymakers to navigate India\u0026rsquo;s evolving energy landscape and fulfil its global climate commitments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eGenerative AI tools were used solely for language editing and formatting purposes. They were not used for conceptual development, data analysis, or interpretation. The authors take full responsibility for the content of this manuscript.\u003c/p\u003e\u003ch2\u003eFunding -\u003c/h2\u003e \u003cp\u003eThis research received \u003cb\u003eno external funding\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eConflict of Interest - The authors declare that they have \u003cb\u003eno\u003c/b\u003e known competing financial or non-financial interests that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003cp\u003eEthics Declaration -\u003cb\u003eNot applicable\u003c/b\u003e. This study does not involve human participants, animals, or clinical data.\u003c/p\u003e \u003cp\u003eConsent to Participate - \u003cb\u003eNot applicable\u003c/b\u003e. This study does not involve human participants.\u003c/p\u003e \u003cp\u003eConsent to Publish - Not applicable. This manuscript does not contain any individual person\u0026rsquo;s data in any form.\u003c/p\u003e \u003cp\u003eClinical Trial Registration - Not applicable. This study is not a clinical trial.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author declares no conflicts of interest financial or non-financial related to the content of this manuscript. Generative AI tools were used solely for language editing and formatting and not for conceptual development, analysis, or interpretation. The author reviewed and verified all content and assumes full responsibility for the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. The data were obtained from publicly accessible secondary sources including the World Bank, International Energy Agency, and Reserve Bank of India.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAguirre, M., and G. 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Wang. 2024. \u0026ldquo;The Impact of Digitalization on Energy Companies\u0026rsquo; Green Transition: Transmission Mechanisms and Empirical Findings.\u0026rdquo; \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e 392: 136057. https://doi.org/10.1016/j.jclepro.2024.136057.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"BVAR, renewable energy, India, climate policy, macroeconomic forecasting","lastPublishedDoi":"10.21203/rs.3.rs-8720414/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8720414/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndia’s economic growth has long been intertwined with environmental challenges, particularly as it seeks to align development goals with climate commitments under the Paris Agreement. Despite increasing investments in green finance and renewable energy, structural barriers persist, limiting the pace of energy transition. This paper provides new empirical insights into the dynamic interactions between macroeconomic, environmental, and energy variables in India using a Bayesian Vector Autoregression (BVAR) framework over the period 2000–2020. Major findings include stagnant renewable energy consumption (REC) through 2027 amid rising CO₂ emissions and declining R\u0026amp;D spending, a negative impact of PM2.5 pollution and CO₂ emissions on REC, and strong path dependency in REC trends. Impulse response functions reveal that foreign direct investment (FDI) initially suppresses but later promotes REC, while paradoxical effects between R\u0026amp;D spending and environmental trade highlight misaligned policy incentives. To support India’s Viksit Bharat 2047 vision, the study recommends targeted reforms, including redirecting FDI toward renewable projects, boosting innovation funding for energy technologies, and realigning environmental trade policies to enhance energy transition outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eJEL Classification\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e : E31, Q54, C32, O13\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Macroeconomic Environmental Interactions and the Path of India’s Energy Transition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 10:17:10","doi":"10.21203/rs.3.rs-8720414/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-13T08:43:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T06:31:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T10:26:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253214997359157225418522673588043626035","date":"2026-03-11T16:00:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168613289793001681386868120769747225071","date":"2026-03-09T10:46:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315576730087576957898263798556768575174","date":"2026-03-09T06:52:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-09T05:29:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T05:08:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-12T14:40:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-02-12T14:35:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bd04a637-a014-41dc-bd4e-062aad6fc2b3","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T10:17:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 10:17:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8720414","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8720414","identity":"rs-8720414","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}