Navigating Climate Finance Landscape in ASEAN

Author

Pinandito Wisambudi

Published

April 24, 2024

Key Findings

Indonesia, the highest emitter in ASEAN, receives the most GCF funding, highlighting the focus on mitigation rather than adaptation efforts.

Climate vulnerability index in ASEAN appears not to be the primary driver of GCF disbursements, despite categorized as one of the most vulnerable region by UNFCCC.
ASEAN countries with unclear climate finance needs such as the Philippines, Malaysia, Thailand and Myanmar receive less funding, indicating the need of institutional capacity support.

Introduction

According to UNFCCC (2022), Southeast Asia is considered as one of the most vulnerable regions in the world due to climate change. Mobilizing climate finance to this region is crucial, not only because these countries are vulnerable to climate impacts but also because ASEAN countries are mainly developing countries that are still struggling with limited resources to adapt to and mitigate the effects of climate change. In this context, it’s crucial to explore the climate finance gap in ASEAN, the mobilization of funds from institutions like the Green Climate Fund (GCF) within ASEAN, and how emissions and vulnerability index in ASEAN countries correlate with the current allocation of GCF resources. Understanding these interactions is crucial for effectively addressing the climate challenges faced by the region.

ASEAN Countries Climate Finance Gap

Figure 1: Climate Finance Needs to Achieve NDC by Country

Source: ADB (2023), UNFCCC (2022), Indonesia (2022), Cambodia (2022), Lao PDR (2021), Reuters (2019), and VNA (2021)

Note: (1) Lao PDR does not disclose its public funding for climate change. (2) Climate finance needs in the Philippines are estimated based on the infrastructure finance need to adapt to climate change according to the Philippines government. (3) Thailand has not communicated its climate finance needs. (4) There is no data for Myanmar, Malaysia, Singapore, and Brunei.

The lack of climate financing in ASEAN countries poses a challenge in dealing with the impact of climate change in the region. While some countries have taken steps to allocate resources for climate adaptation and mitigation, these efforts often fall short of what’s needed. As shown in Figure 1, the public funds allocated by ASEAN countries alone are not enough to tackle the climate finance needs. This shortage emphasizes the need to gather resources from global funds such as Multilateral Development Banks (MDBs), bilateral agreements, and private investments.

Furthermore, securing climate funding becomes more complex due to data and transparency issues in certain ASEAN countries. For example, countries like the Philippines, Thailand, Malaysia, and Myanmar have yet to disclose information regarding their climate financing needs to reach their climate goals. This lack of clarity highlights the difficulties these countries encounter in determining and quantifying their needs, indicating a requirement for capacity-building initiatives to enhance their capabilities in accessing and utilizing climate funds effectively. Bridging this gap will require support from governments, international bodies, and civil society groups to enhance data collection practices, reporting systems, and institutional capacities for managing climate finances across the ASEAN region.

Green Climate Funds Distribution in ASEAN

The GCF is an international fund established by the UNFCCC to assist developing countries in adaptation and mitigation practices to fight climate change. In this section, the distribution of GCF in ASEAN as the primary international funding for climate change is assessed.

Distribution by Country

Figure 2: Distribution of GCF Financing by Country

Source: Green Climate Fund (2023)

Figure 3: GCF Financing by Country

Source: Green Climate Fund (2023)

Figures 2 and 3 above illustrate the distribution of funds in ASEAN countries. The data shows that more than a third of GCF financing in this region goes to Indonesia, with a total of 503.9 million USD. The lowest GCF receiver is Myanmar with 4.1 million USD. Based on this data, it can be concluded that the four lowest GCF receivers are countries that have not explicitly shared their climate finance needs. The top four highest already communicated their climate finance needs on their Nationally Determined Contribution (NDC) document submitted to the UNFCCC.

Distribution by Project Theme

Figure 4: Distribution of Finance by Project Theme

Source: Green Climate Fund (2023)

Figure 5: Proportion of Financing by Project Theme by Country

Source: Green Climate Fund (2023)

Figures 4 and 5 highlight the distribution between mitigation, adaptation, and cross-cutting projects funded by the GCF in Southeast Asia. Data shows that mitigation project accounts for 56.4% of GCF projects in ASEAN countries. Followed by cross-cutting (28.6%) and adaptation (15%). While adaptation finance is still below cross-cutting or mitigation finance, ASEAN’s adaptation finance proportion is higher than the world average, which is below 10%. However, considering how vulnerable the region is, according to the UNFCCC, adaptation finance in Southeast Asia should be increased.

Climate Finance Drivers in ASEAN

To better understand and potentially enhance the alignment between funding and regionale needs, the underlying drivers of climate finance distribution within ASEAN are explored in this section, specifically looking at the correlation between the GCF financing with greenhouse gas emissions and climate vulnerability index.

Figure 6: GCF Financing vs GHG Emissions

Source: Global Carbon Budget (2023) & Green Climate Fund (2023)

Figure 6 displays the correlation between GCF financing against greenhouse gas (GHG) emissions in Southeast Asian countries. Indonesia stands out in the graph as both the top emitter and the top beneficiary of GCF support. This correlation highlights an approach by the GCF in directing funds to where they can have the impact of addressing emissions at their primary source in the region. By prioritizing funds to where the emissions are largest, GCF financing appears to be aligned with the goal of achieving a proportionate response to the emissions challenge within ASEAN, providing focused financial support to Indonesia’s efforts in reducing its GHG emissions, which is also critical for the region’s collective environmental health.

Figure 7: GCF Financing vs Climate Vulnerability

Source: Notre Dame (2023) & Green Climate Fund (2023)

Figure 7 shows that the climate vulnerability index is not the primary driver of climate financing in the region. For example, countries like Myanmar, which has higher vulnerability scores, have received less funding from the GCF than Thailand. This implies that factors beyond vulnerability, such as emission levels, the effectiveness of mitigation and adaptation strategies, or the ability to implement funded projects, are also influencing how GCF resources are distributed. With the rising climate impacts felt in the region, GCF should reassess how the climate vulnerability index impacts the climate finance disbursement. Therefore, those most at risk can better prepare themselves for climate challenges.

Conclusion

In summary, the allocation of climate finance within ASEAN does not fully correspond with the region’s needs or vulnerabilities. While there is a direct correlation between emissions and finance distribution, the climate vulnerability index appears not to have a significant driver in assessing finance disbursement in Southeast Asia despite the UNFCCC identifying it as one of the most vulnerable regions in the world. Additionally, there is also a visible trend where countries receiving the least funding are those that have not outlined their climate finance needs, indicating a potential lack of capacity to identify and communicate those needs. Strengthening the institutional competence of these countries to communicate their financial needs for climate action accurately is critical. Therefore, ASEAN countries can ensure that funding in this region is not only fair but also utilized effectively, thus shaping the path to a resilient and sustainable future for ASEAN.

References

UNFCCC. (2022). Technical Assessment of Climate Finance in Southeast Asia. UNFCCC. https://unfccc.int/sites/default/files/resource/J0008_UNFCCC_NBF_TA_Climate_Finance_SEA_Web_v17.pdf

GCF. (2023). Open Data Library. GCF. https://data.greenclimate.fund/public

Global Carbon Budget. (2023). Annual CO2 Emissions. Our World in Data. https://ourworldindata.org/grapher/annual-co2-emissions-per-country

ADB. (2023, August). Climate Finance Landscape of the Asia and the Pacific. ADB. https://www.adb.org/sites/default/files/publication/901611/climate-finance-landscape-asia-pacific.pdf

Reuters. (2019). Philippine Congress approves $72.15 billion national budget for 2019. Reuters. https://www.reuters.com/article/idUSKCN1PX1ID/

The Government Public Relations Department Thailand. (2024). Value of the national budget in Thailand from 2018 to 2025. Statista. https://www.statista.com/statistics/1114612/thailand-national-budget-value/

VNA. (2021). NA approves resolution on 2019 State budget balance. VNA. https://en.vietnamplus.vn/na-approves-resolution-on-2019-state-budget-balance/205395.vnp

Kingdom of Cambodia. (2022). Cambodia’s Updated Nationally Determined Contribution. UNFCCC. https://unfccc.int/sites/default/files/NDC/2022-06/20201231_NDC_Update_Cambodia.pdf

Lao People’s Democratic Republic. (2021). Nationally Determined Contribution (NDC). UNFCCC. https://unfccc.int/sites/default/files/NDC/2022-06/NDC%202020%20of%20Lao%20PDR%20%28English%29%2C%2009%20April%202021%20%281%29.pdf

Republic of Indonesia. (2022). Enhanced Nationally Determined Contribution. UNFCCC. https://unfccc.int/sites/default/files/NDC/2022-09/23.09.2022_Enhanced%20NDC%20Indonesia.pdf

Notre Dame. (2024). ND GAIN Country Index. Notre Dame. https://gain.nd.edu/our-work/country-index/

--- title: "Navigating Climate Finance Landscape in ASEAN" author: "Pinandito Wisambudi" date: "April 24, 2024" format: html echo: false toc: true toc-expand: 2 toc-location: left code-tools: true link-external-icon: true link-external-newwindow: true --- ## Key Findings - Indonesia, the highest emitter in ASEAN, receives the most GCF funding, highlighting the focus on mitigation rather than adaptation efforts. ```{=html}  ``` - Climate vulnerability index in ASEAN appears not to be the primary driver of GCF disbursements, despite categorized as one of the most vulnerable region by UNFCCC. - ASEAN countries with unclear climate finance needs such as the Philippines, Malaysia, Thailand and Myanmar receive less funding, indicating the need of institutional capacity support. ## Introduction According to UNFCCC (2022), Southeast Asia is considered as one of the most vulnerable regions in the world due to climate change. Mobilizing climate finance to this region is crucial, not only because these countries are vulnerable to climate impacts but also because ASEAN countries are mainly developing countries that are still struggling with limited resources to adapt to and mitigate the effects of climate change. In this context, it's crucial to explore the climate finance gap in ASEAN, the mobilization of funds from institutions like the Green Climate Fund (GCF) within ASEAN, and how emissions and vulnerability index in ASEAN countries correlate with the current allocation of GCF resources. Understanding these interactions is crucial for effectively addressing the climate challenges faced by the region. ## ASEAN Countries Climate Finance Gap #### Figure 1: Climate Finance Needs to Achieve NDC by Country ```{r echo = FALSE, message = FALSE, warning = FALSE} library(tidyverse) library(dplyr) library(readr) library(readxl) library(here) library(ggplot2) ASEAN_Climate_Finance <- here("Final data", "ASEAN Climate Finance.xlsx") %>% read_excel() colnames (ASEAN_Climate_Finance) [1] <- "Country" colnames (ASEAN_Climate_Finance) [2] <- "climate_finance_need" colnames (ASEAN_Climate_Finance) [3] <- "public_climate_finance" colnames (ASEAN_Climate_Finance) [4] <- "climate_finance_gap" colnames (ASEAN_Climate_Finance) [5] <- "public_climate_finance_percentage" colnames (ASEAN_Climate_Finance) [6] <- "public_budget" colnames (ASEAN_Climate_Finance) [7] <- "public_budget_percentage" ASEAN_Climate_Finance <- ASEAN_Climate_Finance %>% mutate( climate_finance_gap = climate_finance_need - public_climate_finance, total_finance = climate_finance_need, # This remains the total need pct_public = public_climate_finance / total_finance * 100, pct_gap = climate_finance_gap / total_finance * 100 ) ggplot(data = ASEAN_Climate_Finance) + geom_bar(aes(x = reorder(Country, -climate_finance_need), y = climate_finance_need, fill = "Climate Finance Gap"), stat = "identity") + geom_bar(aes(x = reorder(Country, -climate_finance_need), y = public_climate_finance, fill = "Public Finance"), stat = "identity") + geom_text(aes(x = reorder(Country, -climate_finance_need), y = public_climate_finance/2, label = sprintf("%.1f%%", pct_public)), vjust = 1.3, color = "white") + geom_text(aes(x = reorder(Country, -climate_finance_need), y = public_climate_finance + climate_finance_gap/2, label = sprintf("%.1f%%", pct_gap)), vjust = 1, color = "white") + scale_fill_manual(values = c("Public Finance" = "red4", "Climate Finance Gap" = "indianred1")) + labs(x = "Country", y = "Finance (Billion USD)", fill = "Finance Type") + coord_cartesian(ylim = c(0, 30)) + theme_classic() ``` *Source: ADB (2023), UNFCCC (2022), Indonesia (2022), Cambodia (2022), Lao PDR (2021), Reuters (2019), and VNA (2021)* *Note: (1) Lao PDR does not disclose its public funding for climate change. (2) Climate finance needs in the Philippines are estimated based on the infrastructure finance need to adapt to climate change according to the Philippines government. (3) Thailand has not communicated its climate finance needs. (4) There is no data for Myanmar, Malaysia, Singapore, and Brunei.* The lack of climate financing in ASEAN countries poses a challenge in dealing with the impact of climate change in the region. While some countries have taken steps to allocate resources for climate adaptation and mitigation, these efforts often fall short of what's needed. As shown in Figure 1, the public funds allocated by ASEAN countries alone are not enough to tackle the climate finance needs. This shortage emphasizes the need to gather resources from global funds such as Multilateral Development Banks (MDBs), bilateral agreements, and private investments. Furthermore, securing climate funding becomes more complex due to data and transparency issues in certain ASEAN countries. For example, countries like the Philippines, Thailand, Malaysia, and Myanmar have yet to disclose information regarding their climate financing needs to reach their climate goals. This lack of clarity highlights the difficulties these countries encounter in determining and quantifying their needs, indicating a requirement for capacity-building initiatives to enhance their capabilities in accessing and utilizing climate funds effectively. Bridging this gap will require support from governments, international bodies, and civil society groups to enhance data collection practices, reporting systems, and institutional capacities for managing climate finances across the ASEAN region. ## Green Climate Funds Distribution in ASEAN The GCF is an international fund established by the UNFCCC to assist developing countries in adaptation and mitigation practices to fight climate change. In this section, the distribution of GCF in ASEAN as the primary international funding for climate change is assessed. ### Distribution by Country #### Figure 2: Distribution of GCF Financing by Country ```{r} GCF_ASEAN <- here("Final data", "GCF ASEAN.xlsx") %>% read_excel() colnames (GCF_ASEAN) [5] <- "Country" colnames (GCF_ASEAN) [7] <- "Sector" colnames (GCF_ASEAN) [8] <- "Theme" colnames (GCF_ASEAN) [11] <- "GCF_financing" # Sum the financing by theme Theme_GCF_ASEAN <- aggregate(GCF_financing ~ Theme, data = GCF_ASEAN, FUN = sum) # Calculate percentages Theme_GCF_ASEAN$Percentage <- with(Theme_GCF_ASEAN, GCF_financing / sum(GCF_financing) * 100) # Sum the financing by theme Country_GCF_ASEAN <- aggregate(GCF_financing ~ Country, data = GCF_ASEAN, FUN = sum) # Calculate percentages Country_GCF_ASEAN$Percentage <- with(Country_GCF_ASEAN, GCF_financing / sum(GCF_financing) * 100) # Reorder the Country factor levels by Percentage Country_GCF_ASEAN$Country <- factor(Country_GCF_ASEAN$Country, levels = Country_GCF_ASEAN$Country[order(Country_GCF_ASEAN$Percentage, decreasing = FALSE)]) # Create a pie chart ggplot(data = Country_GCF_ASEAN, aes(x = "", y = GCF_financing, fill = Country)) + geom_bar(stat = "identity", width = 1) + coord_polar(theta = "y") + geom_text(aes(label = sprintf("%.1f%%", Percentage)), position = position_stack(vjust = 0.5), color = "white") + theme_void() + labs(fill = "Country") + scale_fill_brewer(palette = "Blues") ``` *Source: Green Climate Fund (2023)* #### Figure 3: GCF Financing by Country ```{r} # Create a bar chart ggplot(data = Country_GCF_ASEAN, aes(x = Country, y = GCF_financing / 1e6, fill = Country)) + geom_bar(stat = "identity", color = "navy") + geom_text(aes(label = scales::comma(GCF_financing / 1e6)), vjust = -0.3, color = "black") + scale_fill_brewer(palette = "Blues") + labs( x = "Country", y = "GCF Financing (Million USD)", fill = "Country") + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) ``` *Source: Green Climate Fund (2023)* Figures 2 and 3 above illustrate the distribution of funds in ASEAN countries. The data shows that more than a third of GCF financing in this region goes to Indonesia, with a total of 503.9 million USD. The lowest GCF receiver is Myanmar with 4.1 million USD. Based on this data, it can be concluded that the four lowest GCF receivers are countries that have not explicitly shared their climate finance needs. The top four highest already communicated their climate finance needs on their Nationally Determined Contribution (NDC) document submitted to the UNFCCC. ### Distribution by Project Theme #### Figure 4: Distribution of Finance by Project Theme ```{r} # Create a pie chart ggplot(data = Theme_GCF_ASEAN, aes(x = "", y = GCF_financing, fill = Theme)) + geom_bar(stat = "identity", width = 1) + coord_polar(theta = "y") + geom_text(aes(label = sprintf("%.1f%%", Percentage)), position = position_stack(vjust = 0.5)) + theme_void() + labs(fill = "Finance Theme") + scale_fill_brewer(palette = "Greens") ``` *Source: Green Climate Fund (2023)* #### Figure 5: Proportion of Financing by Project Theme by Country ```{r echo = FALSE, message = FALSE, warning = FALSE} financing_data <- GCF_ASEAN %>% group_by(Country, Theme) %>% summarise(Total_Financing = sum(GCF_financing, na.rm = TRUE)) %>% pivot_wider(names_from = Theme, values_from = Total_Financing, values_fill = list(Total_Financing = 0)) # Calculate the proportion of each theme within each country financing_data <- financing_data %>% mutate(Total = `Adaptation` + `Cross-cutting` + `Mitigation`) %>% mutate_at(vars(`Adaptation`, `Cross-cutting`, `Mitigation`), ~ . / Total) # Gather the data for plotting financing_data_long <- financing_data %>% pivot_longer(cols = `Adaptation`:`Mitigation`, names_to = "Theme", values_to = "Proportion") # Create a stacked bar chart ggplot(financing_data_long, aes(x = Country, y = Proportion, fill = Theme)) + geom_bar(stat = "identity") + labs(y = "Proportion") + theme_classic() + scale_fill_brewer(palette = "Greens") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) ``` *Source: Green Climate Fund (2023)* Figures 4 and 5 highlight the distribution between mitigation, adaptation, and cross-cutting projects funded by the GCF in Southeast Asia. Data shows that mitigation project accounts for 56.4% of GCF projects in ASEAN countries. Followed by cross-cutting (28.6%) and adaptation (15%). While adaptation finance is still below cross-cutting or mitigation finance, ASEAN's adaptation finance proportion is higher than the world average, which is below 10%. However, considering how vulnerable the region is, according to the UNFCCC, adaptation finance in Southeast Asia should be increased. ## Climate Finance Drivers in ASEAN To better understand and potentially enhance the alignment between funding and regionale needs, the underlying drivers of climate finance distribution within ASEAN are explored in this section, specifically looking at the correlation between the GCF financing with greenhouse gas emissions and climate vulnerability index. #### Figure 6: GCF Financing vs GHG Emissions ```{r} ASEAN_emissions <- here("Final data", "total-ghg-emissions.xlsx") %>% read_excel() colnames (ASEAN_emissions) [1] <- "Country" colnames (ASEAN_emissions) [3] <- "Year" colnames (ASEAN_emissions) [4] <- "Emissions" # Assuming the data has columns like 'Country', 'Total_Financing', 'Emissions' # Merge the datasets by country combined_data <- merge(Country_GCF_ASEAN, ASEAN_emissions, by = "Country") combined_data$Emissions <- combined_data$Emissions / 1000000 combined_data$GCF_financing <- combined_data$GCF_financing / 1000000 # Create a scatter plot ggplot(combined_data, aes(x = Emissions, y = GCF_financing, label = Country, color = Country)) + geom_point() + # Use color to differentiate points geom_text(aes(vjust = -0.5, hjust = -0.0), check_overlap = TRUE) + # Optional: Prevent text overlap scale_color_brewer(palette = "Set1") + # Choose a color palette that provides distinct colors for clarity labs(x = "Total GHG Emissions (Million Metric Tons)", y = "GCF Financing (Million USD)", color = "Country") + # Label for the color legend theme_minimal() + xlim(min(combined_data$Emissions, na.rm = TRUE), 2500) + theme(legend.position = "right") ``` *Source: Global Carbon Budget (2023) & Green Climate Fund (2023)* Figure 6 displays the correlation between GCF financing against greenhouse gas (GHG) emissions in Southeast Asian countries. Indonesia stands out in the graph as both the top emitter and the top beneficiary of GCF support. This correlation highlights an approach by the GCF in directing funds to where they can have the impact of addressing emissions at their primary source in the region. By prioritizing funds to where the emissions are largest, GCF financing appears to be aligned with the goal of achieving a proportionate response to the emissions challenge within ASEAN, providing focused financial support to Indonesia's efforts in reducing its GHG emissions, which is also critical for the region's collective environmental health. #### Figure 7: GCF Financing vs Climate Vulnerability ```{r} ASEAN_vulnerability <- here("Final data", "vulnerability_data.xlsx") %>% read_excel() colnames (ASEAN_vulnerability) [1] <- "Country" colnames (ASEAN_vulnerability) [2] <- "vulnerability" combined_data_vulnerability <- merge(Country_GCF_ASEAN, ASEAN_vulnerability, by = "Country") combined_data_vulnerability$GCF_financing <- combined_data_vulnerability$GCF_financing / 1000000 # Create a scatter plot ggplot(combined_data_vulnerability, aes(x = vulnerability, y = GCF_financing, label = Country, color = Country)) + geom_point() + # Use color to differentiate points geom_text(aes(vjust = -0.5, hjust = -0.0), check_overlap = TRUE) + # Optional: Prevent text overlap scale_color_brewer(palette = "Set1") + # Choose a color palette that provides distinct colors for clarity labs(x = "Climate Vulnerability Score", y = "GCF Financing (Million USD)", color = "Country") + # Label for the color legend theme_minimal() + xlim(min(combined_data_vulnerability$vulnerability, na.rm = TRUE), 0.55) + # Set x-axis limits theme(legend.position = "right") ``` *Source: Notre Dame (2023) & Green Climate Fund (2023)* Figure 7 shows that the climate vulnerability index is not the primary driver of climate financing in the region. For example, countries like Myanmar, which has higher vulnerability scores, have received less funding from the GCF than Thailand. This implies that factors beyond vulnerability, such as emission levels, the effectiveness of mitigation and adaptation strategies, or the ability to implement funded projects, are also influencing how GCF resources are distributed. With the rising climate impacts felt in the region, GCF should reassess how the climate vulnerability index impacts the climate finance disbursement. Therefore, those most at risk can better prepare themselves for climate challenges. ## Conclusion In summary, the allocation of climate finance within ASEAN does not fully correspond with the region's needs or vulnerabilities. While there is a direct correlation between emissions and finance distribution, the climate vulnerability index appears not to have a significant driver in assessing finance disbursement in Southeast Asia despite the UNFCCC identifying it as one of the most vulnerable regions in the world. Additionally, there is also a visible trend where countries receiving the least funding are those that have not outlined their climate finance needs, indicating a potential lack of capacity to identify and communicate those needs. Strengthening the institutional competence of these countries to communicate their financial needs for climate action accurately is critical. Therefore, ASEAN countries can ensure that funding in this region is not only fair but also utilized effectively, thus shaping the path to a resilient and sustainable future for ASEAN. ## References UNFCCC. (2022). Technical Assessment of Climate Finance in Southeast Asia. UNFCCC. <https://unfccc.int/sites/default/files/resource/J0008_UNFCCC_NBF_TA_Climate_Finance_SEA_Web_v17.pdf> GCF. (2023). Open Data Library. GCF. <https://data.greenclimate.fund/public> Global Carbon Budget. (2023). Annual CO2 Emissions. Our World in Data. <https://ourworldindata.org/grapher/annual-co2-emissions-per-country> ADB. (2023, August). Climate Finance Landscape of the Asia and the Pacific. ADB. <https://www.adb.org/sites/default/files/publication/901611/climate-finance-landscape-asia-pacific.pdf> Reuters. (2019). Philippine Congress approves \$72.15 billion national budget for 2019. Reuters. <https://www.reuters.com/article/idUSKCN1PX1ID/> The Government Public Relations Department Thailand. (2024). Value of the national budget in Thailand from 2018 to 2025. Statista. <https://www.statista.com/statistics/1114612/thailand-national-budget-value/> VNA. (2021). NA approves resolution on 2019 State budget balance. VNA. <https://en.vietnamplus.vn/na-approves-resolution-on-2019-state-budget-balance/205395.vnp> Kingdom of Cambodia. (2022). Cambodia's Updated Nationally Determined Contribution. UNFCCC. <https://unfccc.int/sites/default/files/NDC/2022-06/20201231_NDC_Update_Cambodia.pdf> Lao People's Democratic Republic. (2021). Nationally Determined Contribution (NDC). UNFCCC. <https://unfccc.int/sites/default/files/NDC/2022-06/NDC%202020%20of%20Lao%20PDR%20%28English%29%2C%2009%20April%202021%20%281%29.pdf> Republic of Indonesia. (2022). Enhanced Nationally Determined Contribution. UNFCCC. <https://unfccc.int/sites/default/files/NDC/2022-09/23.09.2022_Enhanced%20NDC%20Indonesia.pdf> Notre Dame. (2024). ND GAIN Country Index. Notre Dame. <https://gain.nd.edu/our-work/country-index/>