Climate-related risks are increasing both in scale and frequency. According to the COP 30 Scientific Committee (2025), climate-related disasters resulted in global economic losses of approximately USD 320 billion in 2024, continuing an upward trend. Without stronger climate action, global temperatures are projected to rise by 2.6–3.1 °C by 2100, leading to severe consequences such as flooding, extreme heat, and ecosystem disruption. These projections highlight the growing urgency of transitioning toward sustainable pathways.

Such a transition requires substantial financial resources. Climate finance therefore becomes central to enabling mitigation and adaptation efforts. According to the UNFCCC Standing Committee on Finance, climate finance includes local, national, and transnational funding from public, private, and alternative sources that support climate-related actions. While this definition is widely used, there is still no universally accepted interpretation, as the concept continues to evolve across different institutional and policy contexts. Differences in stakeholder priorities, regional needs, and emerging challenges contribute to these variations. Nevertheless, the core idea remains consistent: financial flows directed toward activities that reduce emissions, support adaptation, and enable climate-resilient development.

Global climate finance goals have been developed to support such transitions across countries. Over time, multiple commitments have emerged to mobilise financial resources for climate-related activities, including renewable energy and sustainability-focused projects. However, there is limited clarity at the country level regarding how much finance has been directed to individual economies. In this study, an attempt is made to track these financial inflows in India through sources such as official development assistance, bilateral and multilateral funding, and private investments.

The objective of this research requires the development of a standardized method which will enable us to measure and monitor climate finance inflows that have entered India since 2010 while we assess their global standing. The complete financial inflows will be monitored through all international funding organization websites. The UNFCCC maintains climate finance databases which contain complete information but Indian climate finance information exists as incomplete data with missing components. The research results show that India lacks a complete system for reporting all climate finance inflows which come into the country. The research will evaluate multilateral and bilateral funding organization websites together with project documentation and execution reports to determine the total financial assistance which these funding sources provide.

To check the impact of factors affecting climate finance inflows in India. The raw data shows three types of data issues because of its volatility and structural breaks and years with no incoming data. We model the climate finance inflows by using their annual growth rate. The method provides better results than traditional regression because it reduces spurious regression problems which occur in project-based approval systems that operate over brief periods.

The research uses a bottom up method because there is no centralized dataset which tracks climate finance that countries mobilize jointly. The research traces climate finance flows to India by compiling data from specific global financial facilities and Indian government sources which received international funding and foreign government support instead of using worldwide financial data. The international climate finance research enables researchers to study India environmental funding through project assessment while demonstrating the deficiencies which exist in funding organizations and financing systems regarding their transparency and reporting methods.

Under the GCF, India has accessed both implementation finance and readiness support. The approval of 15 projects with total GCF financing of USD 1.0 billion reflects direct investment in mitigation and adaptation activities with expected measurable climate outcomes. The approved five readiness activities will use USD 5.6 million to develop institutional capacity and policy frameworks and project preparation capabilities.

The low volume of readiness finance compared to project finance demonstrates its function as an enabling tool which supports project execution. Readiness activities serve as essential components which help organizations secure climate financing while improving their capacity to execute major investment projects. The two categories need to be separated because they provide essential information which enables proper evaluation of GCF finance effects on India and assessment of progress toward international climate finance obligations.

NABARD received its National Implementing Entity accreditation in July 2012 to obtain funds from the Adaptation Fund for India. The organization carries out its duties by executing complete project evaluation together with funding distribution activities which it conducts for all Adaptation Fund Board-backed initiatives in the nation. NABARD has established six specific adaptation projects which received total funding of USD 9.853 million according to the graph below which shows that all available funds for the country reached their maximum limit of USD 10 million which the Adaptation Fund Board had established. The projects work together to enhance adaptation capabilities while developing resilient systems which result in measurable success for improving adaptive abilities and benefiting people who take part in the project. NABARD uses Adaptation Fund resources to develop adaptation abilities while safeguarding essential ecosystems and vulnerable communities throughout India.

The portfolio data presented below shows that India maintains a strong relationship with the GEF Trust Fund. The country currently has 46 national projects under the GEF Trust Fund with total financing amounting to USD 412.97 million. India supports 18 additional regional and global projects which receive funding from the identical trust fund and these projects have a total financial backing of USD 250.98 million. National projects demonstrate a high co-financing ratio of 12.65 which shows how projects use both domestic and international funds together with GEF grants. Regional and global projects show a lower co financing ratio of 1.42 which occurs because their funding system includes multiple countries and shared financial resources.

India maintains no active LDCF or SCCF projects because these funding sources exist to assist least developed nations and specific adaptation initiatives. The data demonstrates that the GEF Trust Fund serves as the primary source of environmental funding for India while functioning as a vital multilateral tool that enables both national and regional environmental initiatives.

represents the total climate finance commitments received by India in year t, based on approved project-level funding across selected multilateral sources. These include the Green Climate Fund, Global Environment Facility, Adaptation Fund, and Special Climate Change Fund.

A logarithmic transformation is applied in the form to account for years with zero inflows while retaining all observations in the dataset. The addition of 1 ensures that the transformation remains defined even when no funding is recorded.

The first difference of the logged series, , is used as the dependent variable. This captures the annual growth rate of climate finance inflows rather than their absolute magnitude. The focus on growth rates is appropriate given the discontinuous and project-based nature of climate finance, where large approvals in specific years drive observed fluctuations. This transformation also helps mitigate non-stationarity concerns and allows the analysis to focus on factors influencing changes in inflows over time rather than their levels.

The series below displays substantial annual fluctuations according to figure 1, indicating discontinuous patterns as they are funding specific projects in a cycle. The GEF is the main source of inflows from 2010-2012, specifically in 2013, high rise in inflows was observed from CTF project approvals. The Green Climate Fund becomes a primary funding source starting from 2018. The Green Climate Fund approved USD 200 million and USD 215.6 million in 2022 and 2024, which resulted in total inflows of approximately USD 217.0 million in 2022 and USD 275.9 million in 2024. These represent the highest values in the sample.

The annual funding distribution shows its separate components through Figure 2. The initial funding period shows GEF dominance according to the composition, while the mid-period funding spikes show CTF approval influence, and the following years demonstrate growing GCF funding availability. The Adaptation Fund and SCCF together with other grants contribute to the sample through their limited and infrequent funding throughout the study period. The data shows that multilateral climate finance inflows to India experience both fluctuating patterns and different funding sources during the research period.

Before estimating the time-series model, the stationarity properties of the dependent variable are examined. The Augmented Dickey–Fuller test is applied to the log-transformed series ln(Total_CF + 1).

For the level specification:

The null hypothesis of a unit root cannot be rejected, indicating that the log-transformed series is non-stationary in levels.

For the first-differenced series:

The null hypothesis of non-stationarity again cannot be rejected.

The limited sample size of 15 annual observations, the presence of discrete approval spikes, and structural shifts across years reduce the power of unit root tests in this context. To minimise the risk of spurious regression and to focus on year-to-year variation, the empirical analysis proceeds using the first difference of the log-transformed series, Δln(Total_CF + 1), as the dependent variable. This is evident due to the following data limitations:

Our baseline specification is:

Where we have:

The official climate finance data becomes accessible to after a predetermined time which affects their ability to conduct empirical studies. The table demonstrates that different components of global climate finance become available to the public at different times because each reporting institution and dataset follows its own schedule. Bilateral public climate finance, reported under the framework of the United Nations Framework Convention on Climate Change, typically becomes available several years after the reference year. For instance, flows corresponding to 2021 and 2022 are only finalised in the first quarter of 2025, while data for 2023 will not be available until the first quarter of 2027. In contrast, multilateral public climate finance reported through the OECD Development Assistance Committee exhibits a comparatively shorter lag, generally becoming available within two to three years after the reporting year. Similar reporting delays affect export credits and mobilised private finance data. These staggered release timelines reflect the complexity of national reporting cycles, verification procedures, and harmonisation requirements across institutions. For this study, the presence of such lags necessitates careful consideration of the terminal year included in the time series, as the most recent observations may be provisional or subject to revision. Consequently, interpretations of recent trends in global climate finance must account for the institutional reporting structure that governs data availability.

Table 6 report annual global climate finance flows from 2010 to 2024. The first column presents values in million US dollars, followed by the same figures in absolute US dollar terms for consistency with our India level data. We then compute the natural logarithm of global climate finance and its first difference. The logarithmic transformation reduces scale variation and allows changes to be interpreted proportionally. Taking the first difference of the logged series converts the data into annual growth rates and helps address non stationarity concerns. Since our empirical framework focuses on how changes in global finance influence changes in India’s inflows, modelling growth rather than levels is both econometrically and conceptually appropriate.

The GDP series here is from the World Bank’s World Development Indicators, the GDP measured in constant 2015 US dollars removing the effects of inflation to capture real economic growth over time.

We have employed international crude oil prices, the annual average Brent benchmark price, rather than domestic fuel prices in India as India is a net importer of crude oil and cannot determine international oil prices. Consequently, domestic fuel price movements largely reflect fluctuations in global crude markets, subject to taxes and policy adjustments. For the purposes of our empirical model, we are not attempting to capture domestic fuel pricing policy, but rather broader global energy market conditions.

We use annual total carbon dioxide emissions data for India which Our World in Data obtained from the Global Carbon Project dataset to measure the environmental pressure variable. The series captures territorial CO₂ emissions from fossil fuel combustion and industrial processes on an annual basis. The study uses total emissions data because the researchers want to determine whether climate finance responds to national-level environmental pressure and mitigation requirements. Total emissions act as the better metric because climate finance allocations depend on both the total emissions and the potential for emissions reduction. The emissions series undergoes a natural logarithm transformation which the model uses to handle other macroeconomic variables within its framework. This method enables growth-based analysis of the variable while it also assists in resolving non stationarity issues present in the level series.

The PAT variable is constructed as a binary indicator equal to one for years 2013 onward, corresponding to the operational phase of the Perform Achieve Trade mechanism, and zero otherwise. The dummy variable captures potential policy signalling effects associated with the introduction of a market based energy efficiency instrument. By distinguishing between pre and post implementation periods, the variable allows us to test whether the presence of a formal mitigation mechanism is associated with changes in climate finance inflows, controlling for global financial conditions and macroeconomic factors.

The heteroskedasticity robust estimates confirm that oil price growth remains positive and statistically significant across specifications which include the 2020 outlier exclusion. The energy price channel maintains its permanent strength because of this outcome. The results therefore suggest that climate finance inflows to India over 2011 to 2024 are more closely associated with global energy market conditions than with aggregate global climate finance expansions or domestic emissions growth. The organization of inflows shows higher responsiveness to shifts in energy economics and transition incentives than to indicators of required mitigation efforts and total global funding resources. The actual versus fitted values plot in Figure 4 illustrates the predictive performance of Model 5. The model successfully captures most climate finance growth patterns because most observations remain close to the 45 degree reference line. The line shows residual volatility which increases during years with significant inflow changes. The visual alignment demonstrates that the regression results show high explanatory ability.