When addressing the issue of global climate change, it is crucial to focus on the energy strategies of developing nations, which are predominantly aimed at fostering economic development. India, a major emerging economy and one of the world’s largest greenhouse gas emitting countries, represents a crucial case for climate change policy research. This study examines India’s prospects of reaching carbon neutrality by 2070 and the obstacles to achieving this goal by looking at main socio-economic and institutional factors. Although existing studies provide extensive qualitative analysis of the phenomena in question, the quantitative assessment of their external drivers remains limited. The study addresses this gap by conducting an econometric analysis of selected factors and by introducing middle-class income as a new explanatory variable for India’s long-term emission trends. Methodologically, the research employs the Autoregressive Distributed Lag (ARDL) model to examine the dynamic impact of the selected factors on the intensity of India’s aggregate emissions. The proposed explanatory variables include: the share of forest cover; the share of renewable energy in electricity generation; and the income of the middle-class in India. The analysis shows that each of these factors has a significant impact on the dependent variable. This impact can be immediate or delayed, and it can vary in magnitude and direction. The influence of middle-class income dynamics seems to be the least consistent, while the expansion of renewable energy in electricity generation shows the most pronounced delayed effect. The findings reveal contradictions between national climate policies and socio-economic priorities. They highlight the challenge of balancing low-carbon goals with rapid economic growth and demographic change.

ARDL model, Climate policy, Energy transition, India, Middle class, Socio-economic development.

The far-reaching economic implications of climate change have recently become more apparent and more accurately assessed. Since 2000, climate-related natural disasters have caused global economic losses exceeding $3.6 trillion. According to the World Economic Forum (2024), the economic damage has risen from approximately $450 billion in 2000–2004 to over $1 trillion between 2020 and 2024. Given the global scope of the issue, increased attention is focused on the long-term effects, advantages, and constraints of national climate policies.

In middle-income countries, limited financial resources make it more important to implement economic development strategies than to contribute to global climate change mitigation efforts. (Mohseni-Cheraghlou & Evans, 2023). Broadly defined, these strategies aim to promote economic growth and reduce poverty, which may conflict with goals of climate change mitigation. Moreover, development priorities frequently rely on industrial production, and the high carbon intensity of manufacturing in developing countries may cause rather strong resistance to implementing emission-reduction measures (Mattoo & Subramanian, 2013).

Overall, middle-income countries play an important role in the global climate agenda but it is essential to be aware of their internal diversity. For some countries, mainly upper-middle income ones, addressing fundamental economic development challenges has led to a shift towards adapting climate policies to new realities. China is a notable example of this: between 1980 and 2022, the country accounted for about 75% of the global reduction in extreme poverty. Over this period, the number of people in China living on less than $1.90 per day fell by 770 million (World Bank & Development Research Center of the State Council, 2022). The remarkable progress has served as a catalyst for broader policy discussions regarding China’s long-term commitments to achieving carbon neutrality by 2060.

For other countries, where the primary emphasis remains on economic development, the climate implications of growth-oriented policies are yet to be fully assessed. At the same time, economic growth accompanied by rising incomes and improved living standards inevitably leads to increased consumption, higher energy demand and a subsequent rise in greenhouse gas emissions.

India, one of the world’s fastest-growing large economies, presents a compelling case for studying. As of 2023, it accounted for 18% of the global population but contributed only 5% to the increase in global temperatures (United Nations Environment Programme, 2023). Classified as a lower-middle-income country, India has made significant strides in poverty reduction: the number of people living in extreme poverty fell from 431 million in 1990 to 129 million in 2024. However, the number of people living on less than 6.85 dollars per day was higher in 2024 compared to 1990, due to population dynamics (World Bank, 2024).

Population growth, coupled with economic expansion, inevitably leads to increased energy demand, primarily met by fossil fuels. More specifically, the rapid expansion of the Indian middle class as a proportion of the large and growing population of the country is a significant contributor to the rise of global temperatures. The government’s targeted government policies, including National Food Security Mission, National Rural Livelihood Mission, National Maternity Benefit Scheme, have supported this growth but the problem of rural poverty has remained largely unresolved (Simmons et al, 2014). A successful elimination of poverty in India, as it happened in China at the beginning of the XXI century would imply a significant rise in energy consumption. As empirical evidence suggests, different income groups have different impact on emissions. There is a positive correlation between emissions growth and increasing income concentration in higher-income segments, where consumption tends to be more carbon-intensive (Cappelli, 2024).

Janardhanan (2017) points out that over 60% of India’s population resides in rural areas with limited access to electricity. At the same time, one of the dominant trends in recent years has been urbanization. It is projected that by 2030, approximately 609 million people will live in urban areas, leading to increased electricity demand for air conditioning, lighting, and household appliances (Sartori & Bianchi, 2018).

Against this backdrop, India’s commitment to achieving net-zero emissions by 2070 announced by Prime Minister N. Modi at the 2021 Climate Summit in Glasgow represents a significant contribution to global climate mitigation efforts. This study aims to examine the obstacles and opportunities to India’s achieving carbon neutrality by 2070 taking into account key socioeconomic and institutional factors.

Despite extensive theoretical research into the factors behind India’s climate policy, some aspects remain underexplored through quantitative analysis. Notably, the dynamics of India’s forest cover, a critical component of national climate strategy, have received limited empirical attention. This study attempts to systematize specific external variables for econometric evaluation. The research seeks to move beyond an initial qualitative review of India’s climate policy and to provide a more in-depth analysis of selected drivers using modeling techniques.

Methodologically, the study employs an Autoregressive Distributed Lag (ARDL) model to identify dynamic relationships between dependent and independent variables drawing on the data provided by the World Bank Open Data platform and the Energy Institute Statistical Review of World Energy. Time series data for selected external factors serve as independent variables, while India’s total emissions intensity functions as the dependent one. The variables are selected based on India’s nationally determined contributions (NDCs) submitted in 2022, the period of analysis covers 1990–2023.

The study tests the following hypothesis: in the context of India achieving its 2030 goal of reducing emissions, renewable energy’s contribution to electricity production has both immediate and long-term impacts, while increased forest cover and decreased middle-class income have a longer term effect over a longer time frame.

The study results may provide insights into the role of socio-economic factors in the energy transition processes of other developing countries. India’s climate policy challenges possess specific features that make the findings applicable to the wider context of developing countries. The research methodology is adaptable for analyzing alternative indicators, and calculations can be updated for future time periods.

India’s strategy for achieving carbon neutrality has been shaped in alignment with its national objective of attaining developed country status by 2047 (Garg et al., 2024). The complexities of India’s energy transition have been explored in a number of studies, highlighting economic, social, and institutional challenges as central obstacles.

As noted by Kalghatgi (2024), one of the key economic challenges is formed by the trajectory of India’s economic growth, which continues to drive a steady increase in national energy demand. Primary energy consumption has significantly increased since the 1990s, spurred by economic reforms aimed at accelerating growth (Janardhanan, 2012).

Alekseeva (2022) points out that the rise in energy consumption has been accompanied by increasing carbon intensity within the energy sector largely because coal remains the most accessible energy source and thus a cornerstone of the national energy balance (Fig. 1). The coal industry continues to meet the majority of the country’s energy needs: according to The Energy Institute Statistical Review of World Energy (The Energy Institute, 2024), coal accounted for more than 56% of primary energy consumption and over 75% of electricity generation in 2023. Dorokhina and Sakharov (2023) note that a critical challenge stems from the more rapid increase in coal demand relative to supply. These production–consumption imbalances lead to adjustments in the country’s climate policy. Despite the earlier intentions to phase out several coal–fired power plants, it is expected that India will commission additional generating capacity in the coming years and enhance the efficiency of existing energy facilities.

Figure 1. 

Primary Energy Consumption by Fuel, 1980-2023. Source: The Energy Institute Statistical Review of World Energy, 2024: https://www.energyinst.org/statistical-review

The sustained growth of the industrial sector, according to projections of Mastepanov and Sumin (2020), will continue to serve as a major driver of rising energy consumption in India. This long-term trend highlights one of the main challenges of India’s energy policy: the need to develop a strategy that ensures an adequate supply of energy to support the country’s economic growth goals (Isoaho et al., 2016). At the same time, Bhandari and Dwivedi (2022) argue that the transition period is seen as a crucial phase for India’s economic policy, which requires sustained and robust growth.

Another widely recognized economic challenge concerns the financing of the transition towards net-zero emissions by 2070. India has chosen 2070 as its target year for achieving net-zero emissions, which is later than the target dates of many other countries. This is largely due to the financial constraints that the country faces (Garg et al., 2024). Despite abundant renewables potential, the capital-intensive nature of the RE sector makes financial conditions crucial for its large-scale deployment (Jaspal, 2023). Moreover, as Bhandari and Dwivedi (2022) point out, fossil fuels currently represent a significant share of public revenue in India, necessitating fiscal reform in tandem with energy transition efforts.

Substantial investments are required across various domains, including technological upgrades and infrastructure development. In particular, there is a need to support Indian manufacturers throughout the entire value chain, from the production of core components to the finished product. This was emphasized by Swarnkar and Chauhan. (2022). India remains heavily reliant on imported inputs—including silicon wafers and critical minerals essential to RE manufacturing processes (Indian Institute of Management Ahmedabad, 2024). At the same time, high import duties on components sourced from China and Malaysia, combined with the government’s policy of limiting subsidies and public procurement of domestically produced components, have led to a sharp increase in component prices (Dorokhina & Sakharov, 2023). Overall, Garg et al. (2024) suggest that from 2020 to 2070, India will require $2–2.5 trillion in climate finance (approximately $40–50 billion annually). Projections of the Asia Society Policy Institute (2022) put the total investment needed to meet net-zero goals at $10.1 trillion.

The social dimensions of India’s transition appear to have a dual character. One of the key factors shaping India’s growing energy demand is the projected demographic growth (Shchedrov, 2022). Another important driver, emphasized by Mastepanov and Sumin (2022), is the ongoing effort to address rural energy poverty through the electrification of underserved areas. At the same time, possible regional employment disparities, especially in the eastern “coal belt”, where large-scale job losses are expected, pose serious risks (Shchedrov, 2022). The coal industry is not only a foundation of India’s energy infrastructure; it also has an important social role by providing employment opportunities to large segments of the low-skilled workforce. Pai and Zerrifi (2021) point out that coal mining directly employs more than 700,000 people. The total number, including informal employees, is estimated to be around 2.6 million people (Asia Society Policy Institute, 2022).

A strategically managed and consistent transition policy is likely to contribute to India’s long-term benefits, outweighing its socio-economic costs. Specifically, carbon neutrality scenarios project labor force reallocation: according to Asia Society Policy Institute (2022), the loss of approximately 5 million fossil fuel jobs is expected to be offset by employment growth in manufacturing and services. Positive employment effects are expected in sectors that are part of the green technology supply chain, including construction and infrastructure development, certain extractive industries providing equipment and material supply, and services such as supply chain coordination. Overall, India’s employment gains could amount to around 12 million new jobs by 2060 (Asia Society Policy Institute, 2022) but, to ensure a smooth transition, the country will need targeted investments in social protection and worker retraining (Sharma and Loginova, 2023).

From an institutional standpoint, questions arise regarding the effectiveness of India’s multi-level governance, particularly in the context of energy policy being shaped at both central and state levels. While policy on coal, oil, gas, and nuclear energy is formulated at the national level, electricity policy is under the jurisdiction of individual states. The quality of India’s energy transition is thus shaped by the effectiveness of multi-level governance. Sartori and Bianchi (2018) as well as Swarnakar and Chauhan (2022), emphasize that the main challenge is the lack of oversight mechanisms, and absence of adequate coordination between central and regional governments. Besides, Shukla and Pachour (2023) pointed to the difficulties associated with the timely implementation of delegated legislation. Many of the legislative acts governing the energy sector are outdated and have not been revised in recent years.

Mastepanov and Sumin (2020) reinforce this point and draw attention to the significant institutional fragmentation that continues to shape India’s energy transition. The country’s energy policy is determined by an assemblage of legislative acts, government programs, planning schemes and other documents. These include the Energy Conservation Act (2001), the Electricity Act (2003), updated editions of the National Electricity Plan, the National Action Plan on Climate Change (2008), and the Hydrocarbon Vision 2025 program.

Institutional fragmentation of India’s climate policy relevant to this analysis is a significant challenge. Thus, forest protection in India is implemented through several mechanisms, which include the National Afforestation Programme (2000), the National Mission for a Green India (2014), and the Forest Fire Prevention and Management Scheme (2017). As concerns the growing share of renewable energy in India’s electricity generation, there are several strategic development programmes that stand out. These include the Jawaharlal Nehru National Solar Mission launched in 2010, the Ujwal Discom Assurance Yojna or UDAY started in 2015, and the PM Kusum Scheme promoting the use of solar power in rural areas (2018).

Regulatory gaps are a common occurrence in low- and middle-income countries (Odegedbe, 2023). Today, India has an urgent need for more integrated state-level planning to deal with climate change. However, Radtke, Renn (2024) and Singh (2022), Bhatia (2023), who refer to this situation as a manifestation of resistance to the regime, argue that newly created transition-oriented organizations often find themselves at odds with established bureaucracies and interests that traditionally support specific energy sectors, especially the coal industry.

The challenges related to financial constraints, socio-economic pressures and institutional issues raise the question of how India, one of the world’s largest CO₂ emitters, can design a long-term climate strategy that is economically viable and socially inclusive. The qualitative analysis serves as the foundation for the subsequent quantitative assessment of India’s prospects for reaching carbon neutrality by 2070.

India’s cumulative emissions have still not reached their projected peak, as shown in Fig. 2. This indicates considerable uncertainty about several major external factors over the long term. As discussed earlier, one of these factors is the issue of international climate finance, which complicates direct quantitative assessments of the prospects for India achieving net-zero emissions by 2070. Comparatively, more reliable insights can be drawn about India’s Nationally Determined Contribution (NDC) targets for 2030. These targets are closer in time and represent intermediate steps towards the long-term goal of net zero emissions.

Figure 2. 

Dynamics of Greenhouse Gas Emissions in India. Source: The Energy Institute Statistical Review of World Energy (The Energy Institute, 2024)

One of the key elements of India’s Net Zero 2070 pathway is its NDC target of reducing emissions intensity by 45% relative to 2005 levels. Emissions intensity is defined as the ratio of India’s total greenhouse gas emissions to its gross domestic product. Our study aims to identify the external factors and climate policy measures that may significantly influence this indicator and bring India closer to meeting its 2030 NDC targets. For this purpose, the study assesses the dynamic relationship between a dependent variable and a set of external factors. Specifically, the emissions intensity at time t is influenced by both the current values of explanatory variables and past values of the dependent variable (emissions intensity) and explanatory variables (i.e., t–1, t–2, …, t–l, where l is the lag length indicating delayed effects on the dependent variable).

To model such dynamics, the Autoregressive Distributed Lag (ARDL) approach is employed. The ARDL (p, q₁, q₂, …, q) model, where p is the number of lags for the endogenous variable, and q_i is the number of lags for the i exogenous variable, is specified as:

$y_t=c+\sum _{j=1}^p{\phi }_j{}^{\ast }{y}_{t-j}+\sum _{i=0}^q{\theta }_i\ast x_{t-i}+{\vartheta }_t$

where y is the vector of endogenous variables, x is the vector of exogenous variables, θ and φ are coefficient matrices, and υ(t) is a white noise error term.

Due to data availability, the time frame is limited to 1990–2023. The dependent variable and the external explanatory variables are presented in Table 1.

According to the WIL methodology, the middle class is defined as the population group in the “Middle 40%” segment of the income distribution — 6th, 7th, 8th, and 9th deciles. To further substantiate the rationale for including the middle-class variable, it is essential to highlight the underlying mechanism through which this factor influences emissions. India’s current trajectory of economic growth, combined with sustained efforts to reduce high poverty level, is expected to facilitate a substantial expansion of the middle class (Eggimann & Kendzia, 2022; OECD, 2025) (Fig. 3). The growth of the middle class implies rising demand for electricity and carbon-intensive goods, such as household appliances and private transport. This, in turn, places upward pressure on aggregate emissions. The claim that middle-class expansion drives emissions is well supported by the studies at both the global (Fuhr, 2021; Kartha et al., 2020; Kharas, 2017) and country levels (Grunewald et al., 2012; Pang et al, 2024). Considering India’s large and rapidly growing population and the high carbon intensity of the national economy, the dynamics of the middle-class income share represent a theoretically meaningful predictor of the country’s long-term emissions trajectory.

Figure 3. 

Forecasted Dynamics of the Middle-Class Share in India’s Population. Note: Destitute: < Rs 125000 per annum (~1400 USD); Aspirers: Rs 125,000-500,000 (~1400-5600 USD); Middle class: Rs 500000-3000000 (~5600–34000 USD); Rich: >Rs 3000000 (>34000 USD). Source: CRISIL Market Intelligence & Analytics, 2024

The following hypothesis is tested: regarding the 2030 emissions intensity reduction target, the share of renewables in electricity generation has both immediate and sustained effects, while forest cover expansion and changes in the income share of the middle class have a long-term impact that takes place over a longer time frame. In this context, the delayed effect can be attributed to the time required for forest growth and the gradual increase in income levels until a certain threshold is reached, at which point the factor begins to have a significant impact on consumption patterns. The scientific novelty of this approach lies in its consideration of the evolving income dynamics of the Indian middle class and its specific influence on emission trends related to it.

In practice, ARDL models are typically limited to two or three variables, since they incorporate lags of both dependent and all independent variables. Adding more regressors or lags rapidly increases the number of parameters, which can compromise statistical reliability when the available observations are limited. Accordingly, this analysis focuses on indicators directly linked to India’s climate strategy as outlined in the NDC commitments, along with a measure of the middle class, which holds particular research interest. For potential extensions of the model, the dynamics of the coal industry could represent a relevant factor.

Preliminary time-series analysis (Fig. 4) reveals pronounced trends, confirmed by the Dickey-Fuller test: the null hypothesis of non-stationarity is not rejected at the 5% significance level for any of the variables (Table 2).

Figure 4. 

Graphs of Variables Used in the ARDL Model. Source: complied by the author based on The Energy Institute Statistical Review of World Energy (The Energy Institute, 2024); World Bank Open Data; Bharti et al. (2024).

This emission intensity indicator has exhibited a downward trend since 1992, when emissions intensity peaked. A plausible factor in the subsequent decline is the acceleration of India’s economic growth, whereby GDP growth began to outpace the increase in CO₂ emissions. In recent years, the rate of decline in emission intensity has slowed down, except for the slight increases in that rate observed in 2021 and 2022.

The forest cover in India has shown a steady increase in its share of the total land area, which appears to contradict reports of forest resources depletion. This trend reflects several opposing forces: population growth and urbanization have increased demand for wood and land, while afforestation policies including the Compensatory Afforestation Act of 2016 have supported reforestation efforts. However, official statistics do not capture the changes in forest type and quality: moderate-density forest areas have declined, while open forest areas have expanded, demonstrating possible degradation of Indian forest ecosystems (Kumari et al., 2019).

The share of renewable energy in electricity generation decreased until the early 2000s, mainly due to a decline in the share of hydropower. (Shukla et al., 2007). The expansion of natural gas use during India’s economic reforms in the 1990s involving the New Exploration License Policy and the development of the KG-D6 natural gas field contributed to this trend (Corbeau 2010). However, the composition of India’s energy mix was largely determined by the dominance of coal and the volatility of gas prices.

The income share of the Indian middle class shows a persistent downward trend. The term “middle class” refers to the middle 40% income group, the segment between the bottom 30% and the top 30% of the population in the national income distribution. Previously, from 1951 onward, this share remained stable between 40–46% (Bharti et al., 2024). The current pattern of rising inequality is largely due to income shifts in favor of the wealthiest 10%, 10th decile, whose share has increased from 33.5% in 1990 to 55.7%.

The ARDL approach typically requires transforming non-stationary time series into stationary form through differencing of order d. According to Kalugin, Kim and Petrusevich (2020), this preserves short-term relationships among variables. In this study, second-order differencing (d = 2) is required for most variables to meet stationarity conditions based on the Dickey-Fuller test. The transformed variables are: detr_int_ts, detr_renew_ts, and detr_middle_ts (Table 3).

However, second-order differencing did not fully eliminate non-stationarity in detr_forest_ts, presenting a potential modeling risk. This statistical assumption is nevertheless accepted for the following reasons:

• Integrity and comparability: to maintain uniform data dimensions and relational structure, the same differencing is applied across all variables. If second-order differentiation is required for most variables, especially for the dependent one, this transformation is applied equally to all variables. Applying further transformations only to forest_ts would distort inter-variable relationships.
• Data constraints: reliable, consistent data on India’s forest cover for the required period are limited, and substitution is not feasible. At the same time, the indicator is substantively important for the analysis because forest regeneration remains a central pillar of India’s emissions reduction strategy alongside RE development.
• Trend interpretability: given the persistent upward trend in forest cover, pure detrending would obscure substantial interpretability and undermine the analytical value of the variable.

The chosen transformations strike a practical balance between achieving stationarity and preserving interpretability and data integrity. The forest_ts variable is conceptually harmonized with the broader dataset. Subsequent model outcomes will be evaluated for validity and justification.

It is important to note that the ARDL model accommodates the I (0) and I (1) variables. Since the original variables exhibit integration of order higher than I (1), differencing is necessary to include them in the ARDL framework. However, differencing removes information about long-term equilibrium relationships, therefore cointegration is not tested. This ARDL application focuses on capturing short-term dynamics, which, given the inclusion of multiple lags, still offer valuable insights for the analysis.

Two points complicate the interpretation of the resulting model coefficients. Firstly, as second-order differences are used, the model is presented in “differenced form” (Hyndman & Athanasopoulos, 2018). Coefficients reflect shifts in the rate of change, rather than direct changes in the variables themselves. For instance, if both x and y are upward-trending, a positive coefficient for x implies that an acceleration in the growth of x is associated with an acceleration in the growth of y; a negative coefficient implies that x’s acceleration leads to a deceleration in y. Thus, positive coefficients indicate synchronous movement, while negative ones suggest divergence. This interpretation is particularly useful for assessing India's NDC goals. Assuming the emissions intensity continues to decrease, the model identifies the factors that could most effectively reduce emissions intensity within the target timeline of 2030.

Secondly, emissions intensity is the ratio of total emissions to gross domestic product (GDP), hence the need to identify which is the target of the influence, either emissions or GDP. In this paper, we interpret the regression results as primarily influencing emissions dynamics, and, secondarily, as potentially affecting GDP. With these interpretive considerations in place the modeling results can now be examined.

The ARDL modeling framework allows for the selection of the optimal number of lags for each variable, enabling a more flexible approach to capturing the dynamics of relationships between the variables. Based on the minimum value of the AIC criterion and the selection of the optimal number of statistically relevant regressors, the resulting model takes the form ARDL (3, 3, 4, 1). The residuals of the model were tested for autocorrelation, heteroskedasticity, and normality (Table 4, Fig. 5). The stability of the model coefficients was verified using the CUSUM test: the null hypothesis—the model coefficients remain stable over time—is not rejected at any conventional significance level (p-value = 0.987).

Figure 5. 

ACF and PACF plots constructed for the residuals of the ARDL (3,3,4,1) model. Source: compiled by author based on output data from modeling in RStudio.

The absence of autocorrelation in the residuals is checked visually: the values on the ACF and PACF plots for the model residuals do not exceed the designated corridor of insignificant fluctuations.

The estimated model is statistically significant at any reasonable level: p-value = 0.001, with an F-statistic of 5.913 (degrees of freedom: 14 and 13). Thus, the null hypothesis — that all regressors have coefficients equal to zero — is rejected. The model demonstrates a relatively high explanatory power, with R² = 0.864, the proportion of variance in the dependent variable explained by the model, and the adjusted R² = 0.718, which penalizes for including potentially irrelevant regressors. Therefore, the chosen specification ARDL (3, 3, 4, 1) is accepted, allowing for further interpretation of the estimated relationships.

The results suggest the presence of statistically significant regressors (current or lagged values) across all three external factors. This analysis primarily focuses on the influence of external factors rather than on the autoregressive dynamics of the dependent variable itself. For the regressors discussed below, the levels of significance range from 10% to 0.1% (Table 5). Given the nature of the data, the interpretation of significant regressors emphasizes the signs of the coefficients, i.e. the presence and direction of the effect, and not the precise magnitude of the estimates.

The energy policy of large developing countries is primarily focused on creating conditions for their successful socio-economic development, even in the face of the global climate crisis. India's growing population, the need to overcome widespread poverty, and rapid economic expansion based on carbon-intense production may cause a significant increase in greenhouse gas emissions. That is why it is crucially important to assess the prospects of achieving the country’s declared goal of carbon neutrality by 2070, as articulated in its climate commitments.

The analysis of the conditions that shape India’s energy policy has helped identify its core objective: to ensure sustainable energy security for a rapidly developing economy with high energy intensity of production. The country’s climate policy focuses on increasing the share of renewables in the national energy mix while maintaining and reforming traditional energy sectors. The development of green energy is seen as a tool not only for enhancing energy security and independence but also for stimulating domestic production and addressing energy poverty.

Analysis of the framework of India’s climate policy revealed the key external factors that influence the feasibility of achieving carbon neutrality by 2070. These include financing limitations, socio-economic impacts, and institutional quality.

In the quantitative analysis the carbon intensity indicator was evaluated based on India's updated NDC which represent the intermediate step toward the Net Zero 2070. A set of external factors were proposed that influence the dynamics of this indicator and may be critical for meeting the 2030 NDC commitments. These include the share of forest cover, the share of renewables in electricity generation and the income share of India’s middle class. The first two variables were drawn from the official NDC targets of the country, and the third was based on an analysis of the current study.

ARDL modeling was employed to determine dynamic relationships among the selected variables. The results demonstrate that all three factors significantly affect the dynamics of carbon intensity. However, the direction and duration of their impact vary across variables and may be either immediate or lagged. Specifically, the influence of middle-class income dynamics appears the least conclusive while the impact of increasing renewables share in electricity generation exhibits the most pronounced delayed effect. The original hypothesis is only partly confirmed: although RE expansion does have an immediate and relatively sustained impact on the dependent variable, the anticipated longer-term effects of increased forest cover and declining middle-class income were not supported by the data.

The study shows that India’s climate policy faces considerable constraints in its two key priority areas — expansion of forest cover and development of renewables in power generation. To address challenges of the energy transition, policy recommendations should be grounded in a clearer understanding of the institutional constraints limiting the effectiveness of India’s climate governance. The analysis highlights the four key issues: (1) fragmented climate governance; (2) regime resistance; (3) limited institutional capacity of state governments manifested in weak communication channels both between central and state authorities and between the states; and (4) insufficient oversight over the use of funds and lack of adequate monitoring of program outcomes, seen in forest restoration and renewable energy initiatives.

The institutional environment for India’s energy transition can be strengthened along several feasible lines. These include: (a) enhancing subnational climate planning mechanisms by introducing mandatory annual reporting cycles and creating a unified digital platform to track state-level goals; (b) establishing a formal coordination mechanism for reviewing and aligning policies, plans, and programs across different energy sectors and relevant agencies, supported by periodic cross-sectoral consistency assessments for identifying overlaps or contradictions; and (c) reforming the management of climate-related public finance by creating a transparent national registry of government-managed climate expenditures with clear classification rules, project-level disclosure, and outcome-based performance indicators. In general terms, the main elements of institutional support should include establishing a more transparent and coherent system of financing and monitoring climate initiatives, and strengthening inter-agency coordination, particularly between the central government and state authorities.

As our analysis shows, achieving the 2070 Net Zero Target appears highly challenging given the current trajectory of India’s socio-economic development and institutional performance. The quantitative analysis of external factors confirms a contradiction between India’s national climate ambitions and its socio-economic development strategy. In the context of rapid economic growth and demographic expansion, the question remains: can these policy domains be effectively integrated into a unified strategy? One promising approach involves embedding low-carbon solutions directly into the economic development strategies at the state level. This may allow India to move beyond the forced choice between environmental and socio-economic priorities. The identification of the tools and mechanisms needed for such integration provides a direction for future research.