Can Climate Change Adaptation and Energy Efficiency Drive Economic Growth in Indonesia?

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Erica Ferry Sukma Sitepu, Maria Magdalena Lily Bina, Nyoman Soekarini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5966496/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change and energy efficiency contribute to economic growth. This study aims to examine the impact of climate change and energy efficiency on economic growth across various regions in Indonesia, considering the influence of primary industrial sectors and green investment. The data used is panel data from 13 provinces during the 2014–2023 period. The method used is Mixed-Effect Maximum Likelihood Regression. The research results show that climate change has a significant negative impact on economic growth, especially in regions with a low primary industrial sector. Conversely, energy efficiency demonstrates a significant positive impact, particularly in regions characterized by substantial green investment and a dominant non-primary sector. However, energy efficiency does not exhibit a significant impact in regions with a highly developed primary industrial sector, highlighting the need for modernization within this sector. Meanwhile, inflation was found to have no significant impact on economic growth across all regional categories. Climate change mitigation strategies, including reducing carbon emissions, enhancing energy efficiency, and increasing investment in green infrastructure, are essential for fostering inclusive and sustainable economic growth. Jell Classification: R11, P28, F43,C32 Business and commerce/Economics Business and commerce/Finance Social science/Environmental studies Climate Change Energy Efficiency Economic Growth Mixed-Effect Maximum Likelihood Figures Figure 1 1. Introduction Environmental issues have emerged as a growing global concern due to their profound and far-reaching impact on economic growth (K. Gao & Yuan, 2022 ; Hao et al., 2020 ). Environmental degradation, including deforestation, air pollution, and the overexploitation of natural resources, has the potential to diminish economic productivity, intensify poverty, and exacerbate social inequality. Improper management of natural resources can lead to conflict and economic instability, particularly in developing countries. Consequently, sustainable environmental management is not only essential for preserving ecosystems but also serves as a crucial driver of inclusive economic growth (Asongu & Odhiambo, 2019 ; Ge et al., 2020 ). Climate change has become a widespread problem and a threat to human welfare, development and the environment (Farajzadeh et al., 2023 ; Lu et al., 2019 ). Petrović, ( 2023 ) confirms that climate change can significantly impact economic growth. The rise in carbon dioxide emissions intensifies risks to human survival and undermines environmental sustainability over the long term (Khan et al., 2019 ; Skytt et al., 2020 ). The rise in global temperatures, the increasing frequency of natural disasters, and shifting weather patterns pose significant threats to food production and energy availability (Bolan et al., 2024 ; Duchenne-Moutien & Neetoo, 2021 ). These challenges can hinder economic growth, particularly in countries highly vulnerable to the impacts of climate change, such as Indonesia. Consequently, climate change mitigation has become an urgent priority to ensure sustainable economic development in the face of escalating environmental threats (Ferreira et al., 2020 ; Van Hoa & Limskul, 2013 ). Energy efficiency is one approach that can have a positive impact on economic growth (Adom et al., 2021 ; Li et al., 2023 ; Lin & Zhou, 2022 ). Optimizing energy use, energy efficiency can reduce production costs, increase industrial competitiveness, and open up new investment opportunities (Bonilla-Campos et al., 2020 ; Ibn Batouta et al., 2023 ). In addition, energy efficiency can reduce dependence on fossil fuels, which not only supports the transition to a green economy but also reduces carbon emissions, helping Indonesia contribute to climate change mitigation. In the midst of efforts to mitigate climate change, energy efficiency is a strategic alternative that can encourage sustainable economic growth. Energy efficiency not only functions as a mitigation measure, but is also able to create synergy between environmental protection and economic development (Chen et al., 2024 ; Lin & Zhou, 2022 ). By energy efficiency, Indonesia can harness the potential of technological innovation and cost savings to create a more inclusive and environmentally friendly economy. Indonesia faces significant economic challenges due to climate change, with potential losses stemming from both direct impacts and the costs associated with mitigation strategies. The country's efforts to improve energy efficiency and reduce carbon emissions are crucial in addressing these challenges. Indonesia's commitment to reducing greenhouse gas emissions by 29% by 2030 is expected to result in a GDP loss of around 1.7% compared to the business-as-usual (BAU) scenario. The agriculture sector is projected to experience the most significant impact, with a 13.4% reduction in GDP by 2030, while the energy sector might see a 3.5% increase under mitigation actions (Malahayati & Masui, 2021 ). Compared to other Southeast Asian countries, Indonesia's energy intensity has been more pronounced, with a steady increase until 1999 and a subsequent decline. This trend reflects a shift towards more energy-intensive industries, which is a common pattern in the region. Climate change adaptation and energy efficiency have the potential to drive economic growth in Indonesia. Furthermore, advancements in building energy efficiency can significantly reduce carbon emissions, thereby supporting economic growth through sustainable practices. These strategies align with Indonesia's climate goals and have the potential to stimulate economic activities. While addressing environmental concerns, these measures contribute to a more environmentally friendly development. 2. Literature Review Endogenous growth theory explains that economic growth does not only depend on external factors, but is also influenced by internal factors in an economic system. These factors include technological innovation, human capital accumulation, investment in research and development (R&D), and government policies that support sustainability (Blackburn et al., 2000 ; Sequeira, 2008 ). In this theory, long-term growth results from endogenous processes, such as experience-based learning (learning by doing) and the spread of knowledge, which can increase productivity without limits In an environmental context, endogenous growth theory is relevant to explain how investment in environmentally friendly technology and energy efficiency can encourage economic growth while reducing negative impacts on the environment (Elbasha & Roe, 1996 ; Oueslati, 2002 ). For example, technological innovation in the renewable energy or energy efficiency sector can reduce production costs, increase competitiveness, and create new markets that support the transition to a green economy. Thus, internal factors such as government policies that support research and adoption of low-carbon technologies play an important role in creating sustainable economic growth. This theory highlights the critical role of education and the enhancement of workforce quality as essential components of human capital. A workforce equipped to adopt new technologies and foster innovation plays a pivotal role in driving economic progress. Over time, investments in both human capital and technology generate spillover effects, contributing not only to the acceleration of domestic economic growth but also addressing broader global challenges, including climate change. By incorporating climate change adaptation and energy efficiency into growth strategies, endogenous growth theory offers a robust framework for achieving a harmonious balance between economic development and environmental sustainability. This approach emphasizes that sustainable development can be pursued through investments that promote technological advancement and human capital, ultimately fostering long-term economic resilience and environmental stewardship. Research conducted(Li et al., 2023 ) discusses the role of energy efficiency, natural resource management, financial risk, and technological innovation in driving sustainable economic growth in BRICS countries (Brazil, Russia, India, China, and South Africa). The results show that energy efficiency and technological innovation significantly contribute to long-term economic growth in BRICS countries. However, inefficient management of natural resources can have negative impacts. On the other hand,Lin & Zhou, ( 2022 ) explore the relationship between energy efficiency (EFF) and the quality of economic growth in China using provincial panel data from 2000 to 2017. Energy efficiency significantly improves the quality of economic growth in the eastern region, while in the central and western regions, it reduces the quality of growth. These findings underscore the existence of strong regional differences, driven by different levels of technology, economic structure and resource utilization in each region.Adom et al., ( 2021 ) discusses the relationship between energy efficiency and economic growth in 51 African countries, considering the moderating role of income inequality. This research uses the stochastic frontier analysis (SFA) method to measure energy efficiency and the Generalized Method of Moments (GMM) approach to analyze panel data from 1991 to 2017. The results show that improvements in energy efficiency directly trigger economic growth, but the impact decreases in countries -countries with high-income inequality. On the other hand, research conducted by Petrović, ( 2023 ) explained at the current level of climate change, its impact on global economic growth still tends to be positive on average, but mitigation measures are still needed to avoid negative consequences in the future. Farajzadeh et al., ( 2023 ) analyzes the impact of climate change on economic growth in Asian countries, one of the region’s most vulnerable to climate change. Climate change significantly affects output per worker through three channels: reduced productivity, increased capital depreciation rates, and direct impacts on output levels. Lu et al., ( 2019 ) discusses the impact of climate change on sustainable economic development with a focus on the Nanjing region, China. Increased rainfall and decreased temperatures can reduce economic impacts, while greater temperature variations have negative effects on capital investment and economic development 3. Methods and Data Analysis The objective of this investigation is to analyze the influence of Climate Change Adaptation and Energy Efficiency on the promotion of economic growth. This analysis will describe the data, variables, and methodologies used to attain this aim. The findings are designed to provide insights into how these components can promote sustainable economic development. 3.1. Data This research utilizes secondary data, specifically in the form of panel data. The panel data spans a period from 2014 to 2023. It covers 13 provinces across Indonesia, providing a comprehensive overview of the relevant trends. The data includes various economic and environmental indicators, crucial for the analysis of the study. The primary data source is the Indonesian Central Statistics Agency (BPS). BPS is a reputable institution responsible for collecting and disseminating official statistics in Indonesia. This data enables the research to examine regional variations and patterns over the study period. 3.2. Explained Variable: Economic Growth Economic growth reflects the overall level of economic activity within a specific region. It is often used as a key indicator to assess the economic health and development of a region. Regional economic growth is commonly measured by tracking changes in output over a given period of time. This measurement is typically expressed through the Gross Regional Domestic Product (GRDP), which reflects the total value of goods and services produced in a region. GRDP serves as a comprehensive indicator of economic performance at the regional level. For this research, GRDP is used to assess economic growth in 13 provinces across Indonesia. By examining the GRDP data, the study aims to understand the economic dynamics and growth patterns in these regions over time. Economic growth is an important aspect that is influenced by various factors. Research conducted byLu et al., ( 2019 ) demonstrations that climate change not only damages the environment but also has long-term impacts on the economic and social development of a region. Besides that,Farajzadeh et al., ( 2023 ) confirm the existence of a significant relationship between climate change and economic growth. On the other hand, energy efficiency has also been proven to influence economic growth, as stated in research (Adom et al., 2021 ; Lin & Zhou, 2022 ). These findings are important for providing empirical insight into the relationship between environmental sustainability and economic growth, as well as providing a basis for formulating policies that support sustainable economic growth 3.3. Explanatory Variable Climate Change Climate change is defined as a shift in average temperature and rainfall that occurs over time, which is measured using a statistical approach (Fan et al., 2024 ; Kahn et al., 2021 ; X. Wu et al., 2023 ). Climate change indicators use concepts Wu et al., ( 2023 ), with the main indicators in the form of deviation levels of national temperature and rainfall from historical averages as a basis for identifying changes in climate conditions. The sample data used covers the period 2014–2023 in 13 provinces in Indonesia. However, to measure climate change, historical data on temperature and rainfall from 2000–2023 for the 13 provinces is used. The first step is to measure temperature and rainfall anomalies by subtracting sample data from historical data. The anomaly value is then used to calculate the variance, which is then normalized to support further analysis. $$\:{k}^{*}=\frac{k-\:{k}_{min}}{{k}_{max}-{k}_{min}}$$ 1 where \(\:{k}^{\text{*}}\) describes data normalization of data, k is the variance of temperature and rainfall. \(\:{k}_{max}\) and \(\:{k}_{min}\) each shows the maximum and minimum values ​​of the variance of temperature and precipitation. The second stage involves measuring climate change (CC) by utilizing the results of normalized variance of temperature (TEMP) and precipitation (PCPT), which are then multiplied by weights that have been determined based on data for the 2014 to 2023 period $$\:{CC}_{it}={\omega\:}_{1}{TEMP}_{it}+\:{\omega\:}_{2}{PCPT}_{it}$$ 2 constant \(\:{\omega\:}_{1}\) and \(\:{\omega\:}_{2}\) functions as a weight that describes the impact of temperature and rainfall fluctuations on the financial industry sector. Determination of weight \(\:{\omega\:}_{1}\) and \(\:{\omega\:}_{2}\) This was done by calculating the correlation between normalized deviations in temperature and rainfall and the growth rate of the financial industry. This financial sector growth is represented through changes in the financial sector's contribution to economic growth. Climate change has a significant impact on economic growth, both directly and indirectly. Climate change exhibits an inverted U-shaped effect on economic growth in tropical rainforest and dry climate zones, negatively impacting agriculture and services sectors (Zhao & Liu, 2023 ). Addressing climate change through mitigation and adaptation strategies is crucial to minimizing these adverse impacts and fostering sustainable economic growth Energy Efficiency High pressure to reduce emissions and solve energy problems demands the implementation of more optimal energy efficiency. Green Total Factor Energy Efficiency (GTFEE) is a strategic approach designed to address the energy crisis and climate change while ensuring the achievement of economic output targets (Wu et al., 2024 ). GTFEE is defined as the comparison between the expected energy value and the actual value under conditions of endogenous pollution (Wu et al., 2021 , 2024 ). This ratio reflects the achievement of optimal economic benefits with minimal levels of environmental pollution, which is obtained through a comprehensive analysis of input factors such as capital, labour and energy in the production process (Wu et al., 2024 ). On the input side, increasing GTFEE contributes to reducing energy waste and supporting economic growth, thereby reducing pressure on energy supplies (Hancevic & Sandoval, 2022 ; Wu et al., 2024 ). Meanwhile, on the output side, increasing GTFEE can reduce pollutant emissions and help achieve economic output targets (Gao & Yuan, 2022 ; Wu et al., 2024 ), which can effectively mitigate the impact of climate change on economic development. Measurement Green Total Factor Energy Efficiency (GTFEE) is carried out using the model Super-Efficiency Slacks-Based Data Envelopment Analysis (SBM-DEA) adopted from research (Gao et al., 2022 ; Wu et al., 2021 , 2024 ). The SBM-DEA model was chosen because of its ability to overcome the limitations of conventional DEA methods, especially in analysing actual input and output slack and measuring efficiency under unexpected output constraints (Lee & Kim, 2023 ). The SBM-DEA Model formulation is as follows $$\:minimize\:e=\frac{1-\frac{1}{m}\sum\:_{i=1}^{m}\frac{{s}_{i}^{-}}{{x}_{io}}}{1+\frac{1}{s}\sum\:_{r=1}^{s}\frac{{s}_{r}^{+}}{{y}_{r0}}}$$ 3 Subject to $$\:{x}_{0}=X\lambda\:+{s}^{-}$$ $$\:{y}_{0}=Y\lambda\:-{s}^{+}$$ $$\:{s}^{-}\ge\:0,{s}^{+}\ge\:0,\lambda\:\ge\:0$$ where e is the evaluated DMU efficiency, m and s is the sum of input and output. \(\:{s}^{-}\) and \(\:{s}^{+}\) the representation of slacks in input and output. The input factors used in this research include capital, labour and energy. Regional fixed capital is measured using Gross Fixed Capital Formation (GFCF), while labour is represented by the number of workers in each province. Since provincial level energy consumption data is not available, electricity consumption is used as an energy indicator. Output factors consist of expected output and unexpected output. Expected output is represented through regional economic growth indicators, while unexpected output is measured using the total waste produced. The impact of energy efficiency on economic growth is very significant, because it can reduce operational costs in various sectors, increase industrial competitiveness, and encourage investment in environmentally friendly technologies. Optimizing energy use in reducing inefficient energy consumption, thus encouraging a sustainable economy (Adha et al., 2024 ). 3.4. Other Variable Other variables used in this research are inflation, primary industrial sector and green infrastructure. Inflation (INF) is presented as consumer purchasing power which can influence economic growth (Tillaguango et al., 2024 ). The primary industry variable is represented by the agriculture, livestock, hunting and agricultural services sectors can affect economic growth. Climate change and energy efficiency levels can affect primary sector productivity. On the other hand, research conducted by Soltani et al., ( 2023 ) explains that climate change can affect energy efficiency in the agricultural sector. Green infrastructure is government spending to mitigate climate change (Fan et al., 2024 ). Investment in green infrastructure to encourage economic growth 3.5. Models This research model is a modification of research conducted by Adom et al., ( 2021 ); Farajzadeh et al., ( 2023 ); Lu et al., ( 2019 ); Petrović, ( 2023 ). The model is built as follows $$\:{Y}_{it}=\:{a}_{0}+{a}_{1}{CC}_{it}+{a}_{2}{EE}_{it}+{a}_{3}{pi}_{it}+{a}_{4}{gi}_{it}+{a}_{5}{inf}_{it}+{\epsilon\:}_{it}$$ 4 Equation ( 4 ) explains the relationship between climate change, energy efficiency, primary industry, green investment and inflation on economic growth. \(\:{Y}_{it}\) is economic growth in region i at time t. \(\:{CC}_{it}\) and \(\:{EE}_{it}\) is climate change and energy efficiency in region i at time t. \(\:{pi}_{it}\) and \(\:{gi}_{it}\) is an indicator of the primary sector and green investment in region i at time t. \(\:{inf}_{it}\) is inflation in region i at time t. \(\:{\epsilon\:}_{it}\) defined as a random disturbance term. The model in this study was estimated using the method of Mixed-Effect Maximum Likelihood (ML) Regression, which is designed to simultaneously capture fixed effects and random effects in panel data analysis. This approach allows controlling variations between individuals or groups that are not directly observed while producing more accurate and efficient parameter estimates. This method is also very suitable for use on data with a hierarchical structure or in situations where there is heterogeneity between observation units. In addition, this research also applies heterogeneity analysis to evaluate the impact of climate change and energy efficiency on economic growth under certain conditions. The particular conditions in question involve differences in regional characteristics, namely areas dominated by the Primary Industrial Sector (PI) and the presence of Green Infrastructure (GI). This approach aims to provide a more comprehensive understanding of the relationship between environmental factors and economic growth in various regional contexts. 4. Results and discussion 4.1. Results Based on the results of the analysis using the method of Mixed-Effect Maximum Likelihood (ML) Regression explained that climate change and energy efficiency influence economic growth. On the other hand, primary variables and green investment affect economic growth, but inflation does not affect economic growth. The results of the analysis using the method of Mixed-Effect Maximum Likelihood (ML) Regression can be seen in Table 1 Table 1 Analysis results in Mixed-Effect Maximum Likelihood (ML) Regression Variable Coefficient z-statistics Prob Climate Change -0,001 -2,27 0,016* Energy Efficiency 1,251 2,91 0,004* Primary Industry 0,331 2,36 0,019* Green Investment 0,209 6,48 0,00* Inflation 0,002 0,43 0,668 LR test vs. linear model 0,000* Note: * significant α = 5%, ** significant α = 10% Table 1 shows that climate change has a coefficient of -0.001 and a z-statistic value of -2.77 and a p-value of 0.0016 < α = 5%. This indicates that the relationship between climate change and economic growth is negative and significant at the 95% confidence level. An increase in the intensity of climate change is statistically associated with a decrease in economic growth, although the effect tends to be small. Lu et al., ( 2019 ); Petrović, ( 2023 ) explains that climate change, such as increasing temperatures and changes in rainfall, has a significant impact on economic growth. Changes in temperature and rainfall can have an impact productivity of natural resources, especially in sectors that are highly dependent on climate such as agriculture, fisheries and energy, which ultimately slows down economic growth (Farajzadeh et al., 2023 ; Lu et al., 2019 ). Long term, climate change may affect economic structure by forcing countries to shift focus from climate-based sectors to sectors that are more resilient to environmental change. Nevertheless, this transition requires time and investment, which often limits growth in the short term (Farajzadeh et al., 2023 )​. Endogenous Growth Theory explains that economic growth is influenced by various internal factors. Climate change on key sectors such as agriculture and industry can reduce the level of investment in technology and human resources, which in turn can hamper long-term economic growth. Energy efficiency shows a positive coefficient of 1.251 with a z-statistic value of 2.91 and a p-value of 0.004 < α = 5%. The significant positive relationship between energy efficiency and economic growth shows that increasing energy efficiency makes a positive contribution to economic growth. In other words, efficiency in energy use plays an important role in supporting productivity and encouraging sustainable economic growth. Increasing energy efficiency has been proven to encourage sustainable economic growth by reducing carbon emissions and increasing energy productivity​ (Li et al., 2023 ). Adom et al., ( 2021 ) Energy efficiency plays a crucial role in reducing the wastage of natural resources by optimizing their use and extending their useful life. By minimizing energy consumption, resources are conserved, leading to lower environmental impact. The resulting energy savings can be redirected towards other economic activities, fostering overall economic productivity and sustainability. Endogenous growth theory is relevant to explain how investment in environmentally friendly technology and energy efficiency can encourage economic growth while reducing negative impacts on the environment (Elbasha & Roe, 1996 ; Oueslati, 2002 ). The primary industrial sector also shows a positive relationship with economic growth, with a coefficient of 0.331, a z-statistic value of 2.36, and a p-value of 0.019 < α = 5%. The significant positive relationship between sectors of Primary industry and economic growth shows that the dominance of the primary industrial sector in a region makes a positive contribution to economic growth. The Primary Industry Development Level, which is measured by the development of primary industrial sectors, such as agriculture, forestry and fisheries, influences economic growth (Elzaki, 2024 ; Grabowski & Self, 2023 ). On the other hand, green investment shows a coefficient of 0.209 with a z-statistic value of 6.48 and a p-value of 0.000 < α = 5%. Increasing investment in green infrastructure consistently has a positive impact on economic growth. Green infrastructure development is one of the key factors in supporting sustainable economic growth (Wang et al., 2023 ). Green infrastructure is government spending to mitigate climate change(Fan et al., 2024 ). The analysis reveals that inflation has a positive coefficient of 0.002, with a z-statistic value of 0.43 and a p-value of 0.668, which is greater than the significance level of α = 5%. This indicates that the relationship between inflation and economic growth is statistically insignificant, suggesting that inflation does not exert a substantial influence on economic growth in this context. Furthermore, the likelihood ratio (LR) test comparing the mixed-effects model to the linear regression model yields a probability value of 0.000, affirming that the mixed-effects model is more appropriate for this data than the traditional linear regression model. These results underscore the importance of incorporating both fixed and random effects in the analysis, which enables more robust and precise estimates for understanding the complex dynamics of economic growth. Table 2 Results of heterogeneity analysis Variable Primary Industry Green Investment Low High Low High Climate Change -0,019* [-6,83] (0,000) -0,000* [4,96] (0,000) -0,004* [-2,29] (0,020) -0,000* [2,92] (0,005) Energy Efficiency 1,060* [2,72] (0,008) -0,530 [-1,16] (0,252) 0,879* [2,64] (0,015) 1,076* [2,32] (0,024) Inflation -0,019 [-1,44] (0,156) 0,016 [0,37] (0,713) 0,002 [0,04] (0,968) -0,005 [-0,07] (0,941) Note: * significant α = 5%, ** significant α = 10% […] = z-statistic (…) = Prob. The heterogeneity analysis presented in Table 2 reveals that the impact of climate change on economic growth is contingent on the level of the primary industrial sector and green investment in a region. In regions with a low primary industrial sector, climate change exhibits a significant negative relationship with economic growth, as evidenced by a coefficient of -0.019 (z-statistic − 6.83, p = 0.000). This indicates that the increasing effects of climate change substantially hinder economic growth in these regions, likely due to their limited industrial diversification and greater reliance on sectors vulnerable to environmental disruptions. In contrast, regions with a high primary industrial sector display a much smaller, yet statistically significant, negative effect of climate change, with a coefficient of -0.000 (z-statistic 4.96, p = 0.000). This suggests that these regions may exhibit greater resilience to climate-related impacts, possibly owing to the robustness of the primary industries, which are often more adaptable to environmental fluctuations. Regarding green investment, climate change has a significant negative effect on economic growth at both low (-0.004, z-statistic − 2.29, p = 0.020) and high (-0.000, z-statistic 2.92, p = 0.005) levels of green investment. However, the negative impact of climate change is less pronounced in regions with higher levels of green investment, underscoring the mitigating role that sustainable investment can play in reducing the adverse effects of climate change on economic performance. The impact of energy efficiency on economic growth varies significantly based on the regional characteristics of the primary industrial sector and the level of green investment. In regions with a low primary industrial sector, energy efficiency exhibits a significant positive influence, with a coefficient of 1.060 (z-statistic 2.72, p = 0.008). This suggests that improving energy efficiency in these areas plays a crucial role in boosting economic growth. The positive relationship indicates that regions with less reliance on primary industries can capitalize on energy efficiency improvements to enhance productivity and foster sustainable economic development. The significant effect in low primary industrial regions highlights the potential for energy efficiency to serve as a key driver of economic transformation in these areas. In contrast, regions with a high primary industrial sector do not show a significant relationship between energy efficiency and economic growth. The coefficient for these regions is -0.530 (z-statistic − 1.16, p = 0.252), indicating an insignificant negative impact. This lack of significance may be attributed to the inherent rigidity of primary industrial sectors, which are often less adaptable to the adoption of energy-efficient technologies. These sectors may face challenges in integrating such innovations due to their reliance on traditional, resource-intensive production processes. Regarding green investment, energy efficiency demonstrates a significant positive relationship at both low (0.879, z-statistic 2.64, p = 0.015) and high (1.076, z-statistic 2.32, p = 0.024) investment levels. These results underscore the importance of energy efficiency as a critical factor supporting economic growth in regions that prioritize green investment. Enhanced energy efficiency, coupled with green investment, facilitates the transition to a more sustainable and resilient economy, benefiting regions regardless of their industrial composition. The analysis reveals that inflation does not significantly influence economic growth across different categories of primary industrial sectors or green investment. In regions with a low primary industrial sector, the coefficient for inflation is -0.019 (z-statistic − 1.44, p = 0.156), indicating a weak negative relationship, but this result is not statistically significant. Similarly, in regions with a high primary industrial sector, the coefficient is 0.016 (z-statistic 0.37, p = 0.713), suggesting a negligible positive effect, which is also statistically insignificant. Regarding green investment, the relationship between inflation and economic growth remains insignificant at both low (0.002, z-statistic 0.04, p = 0.968) and high (-0.005, z-statistic − 0.07, p = 0.941) investment levels. These findings suggest that inflation does not play a significant role in driving economic growth within these specific regional categories. The results of the heterogeneity analysis, as depicted in Fig. 1, reveal varying impacts of climate change, energy efficiency, and inflation on economic growth across different quantiles. Specifically, Fig. 1(a) illustrates that climate change consistently exerts a negative effect on economic growth at all quantiles, but this impact intensifies in regions with more advanced economic conditions. The analysis shows a reduction in the coefficient values at higher quantiles, suggesting that the adverse effects of climate change are more pronounced in economically developed regions. This may be attributed to the increased reliance on infrastructure and sectors that are more vulnerable to environmental changes, such as urbanized areas and industries with high carbon footprints. Consequently, the results highlight the compounded challenges climate change poses to economically developed regions. Figure 1(b) demonstrates a positive relationship between energy efficiency and economic growth, with the strength of this relationship increasing at higher quantiles. This pattern suggests that regions with more favorable economic conditions experience greater benefits from improvements in energy efficiency. The enhanced positive effect in economically developed regions may be due to their capacity to implement advanced technologies and optimize resource management practices, which are crucial for improving productivity and fostering sustainable growth. Regions with higher investment capacity are better equipped to adopt energy-efficient technologies and infrastructures, thus driving economic growth more effectively. These findings underscore the pivotal role of energy efficiency as a key driver of economic development, particularly in regions with the financial resources to support green technologies and innovations. Figure 1(c) reveals a non-linear relationship between inflation and economic growth, highlighting varying impacts across different quantiles. At low to middle quantiles, inflation exhibits a weak positive effect on economic growth, suggesting that moderate inflation may be associated with economic expansion in less developed regions. However, as the quantiles increase, the relationship shifts, with inflation turning negative and its impact intensifying in regions with higher economic conditions. This indicates that economically developed regions are more susceptible to the detrimental effects of inflation, possibly due to factors such as higher costs of living, wage pressures, and financial market volatility. The results emphasize that inflation's influence on economic growth is contingent upon the specific economic context and development level of a region. 4.2. Discussion This research demonstrates that climate change, energy efficiency, and inflation significantly influence economic growth, with the impact varying depending on regional structural conditions such as the dominance of the primary industrial sector and the level of green investment. These findings underscore the complex interplay between environmental factors and regional economic dynamics. The influence of climate change, in particular, is shown to have a notable negative relationship with overall economic growth, suggesting that the increasing severity of climate change can impede economic progress. This effect is especially pronounced in regions where the primary industrial sector is less dominant, where vulnerability to climate-related disruptions is higher. However, the negative impact of climate change on economic growth tends to be smaller in regions with a significant primary industrial sector. This may be attributed to the relative resilience of industries within these sectors, which are better equipped to withstand the adverse effects of climate change. As such, regions with a dominant primary industrial base may possess certain structural advantages that mitigate the immediate negative consequences of climate-induced disruptions. Nonetheless, the findings indicate that even in these regions, the long-term effects of climate change could pose a challenge to sustained economic growth if not adequately addressed. Energy efficiency, on the other hand, has been identified as a key factor supporting economic growth. The research highlights that energy efficiency exerts a significant positive influence on economic performance, particularly in regions with low primary industrial sectors and high levels of green investment. This suggests that regions with advanced energy efficiency measures and a strong focus on green investment are better positioned to optimize resource use, leading to enhanced economic growth. The positive relationship between energy efficiency and economic growth points to the potential for energy innovations to act as a catalyst for regional development. In contrast, the impact of energy efficiency is less pronounced in regions dominated by the primary industrial sector. This discrepancy may be attributed to the lower adaptability of industries within these sectors to energy-saving technologies and innovations. The rigidities inherent in primary industries, such as agriculture and extractive sectors, make them less flexible in adopting energy-efficient practices. As a result, while energy efficiency contributes to economic growth in more diversified or green investment-focused regions, its potential in primary industry-dominated regions remains limited unless there is substantial transformation within these sectors. Based on the analysis, several policy recommendations emerge. First, it is imperative for the government to strengthen climate change mitigation strategies, particularly through reducing carbon emissions and protecting vulnerable sectors. Policies should focus on the development of green investments, such as environmentally friendly technologies and infrastructure capable of adapting to climate change. These measures should be prioritized in regions with low primary industrial sectors, where the impact of climate change is more pronounced. This approach will help mitigate the negative effects of climate change on economic growth, enabling regions to pursue more sustainable development trajectories. Moreover, promoting energy efficiency must be a central focus of government policy. Incentives for both the industrial sector and households to adopt energy-saving technologies, such as subsidies for energy-efficient devices or tax credits for renewable energy investments, are essential. Additionally, increasing investment in green infrastructure, particularly in low-emission public transport, renewable energy generation, and efficient waste management systems, will be crucial in supporting long-term sustainable development. These investments will not only improve energy efficiency but also contribute to broader environmental goals, fostering a more resilient and sustainable economy. In regions where the primary industrial sector dominates, the focus should shift toward economic diversification and the integration of green technologies. Policies should prioritize workforce training, incentives for the adoption of low-carbon technologies, and the modernization of production processes to reduce the environmental impact of these industries. While inflation did not show a significant direct influence on the results of this study, maintaining inflation stability remains critical. Prudent monetary policy and the management of basic commodity prices are essential to support consumer purchasing power and ensure continued investment. Furthermore, increased regional collaboration is necessary, enabling regions with low green investment to benefit from the experiences and technological advancements of regions with higher levels of green investment. This approach, encompassing technology transfer, best practices sharing, and public-private partnerships, will promote inclusive and sustainable economic growth and enhance regional resilience to climate change. 5. Conclusion Climate change and energy efficiency significantly influence economic growth, although the extent of their impact varies according to regional characteristics. The findings indicate that climate change negatively affects overall economic growth, particularly in regions with a low primary industrial sector, where the capacity to withstand climate-related disruptions is limited. This relationship suggests that regions with a more diversified or lower reliance on primary industries face greater vulnerabilities to the adverse effects of climate change. Conversely, the negative impact of climate change is less pronounced in regions dominated by primary industrial sectors. This may be attributed to the inherent resilience of such sectors, which can better adapt to climate change due to their operational structure and existing infrastructure. Energy efficiency is identified as a critical driver of economic growth, especially in more developed regions with substantial green investment. These regions demonstrate a higher capacity to leverage energy efficiency measures to enhance productivity and foster the transition to a green economy. However, the influence of energy efficiency is less significant in regions with a strong primary industrial base, suggesting that these sectors require modernization and greater adoption of energy-saving technologies. Based on these findings, the research recommends the implementation of climate change mitigation policies focused on green investment, renewable energy subsidies, and economic diversification. These strategies aim to promote inclusive and sustainable economic growth while strengthening resilience to environmental challenges. Declarations Conflict of interest : The authors declare no conflict of interest Author Contribution Conceptualization, EFSS; methodology, MMLB; software, MMLB; validation, EFSS; formal analysis, NS; data curation, MMLB; writing original draft preparation, NS; writing review and editing, EFSS. All authors have read and agreed to the published version of the manuscript. References Adha R, Hong C-Y, Yang S-F, Muzayyanah S (2024) Re-Unveiling the energy efficiency impact: Paving the way for sustainable growth in ASEAN countries. Sustain Dev 32(5):5812–5824. https://doi.org/https://doi.org/10.1002/sd.3005 Adom PK, Agradi M, Vezzulli A (2021) Energy efficiency-economic growth nexus: What is the role of income inequality? Journal of Cleaner Production , 310 . https://doi.org/10.1016/j.jclepro.2021.127382 Asongu SA, Odhiambo NM (2019) Environmental degradation and inclusive human development in sub-Saharan Africa. 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Econ Anal Policy 78:419–437. https://doi.org/10.1016/j.eap.2023.03.012 Zhao Y, Liu S (2023) Effects of Climate Change on Economic Growth: A Perspective of the Heterogeneous Climate Regions in Africa. Sustain (Switzerland) 15(9). https://doi.org/10.3390/su15097136 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5966496","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":416572180,"identity":"9f05e7ca-75e0-458e-a706-3cd69c0deaf3","order_by":0,"name":"Erica Ferry Sukma Sitepu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYFACxgYGhgoQ4wCIkABxidFyhjQtIF1tqCbgB/zShxs//Jx3OFq+8QDjhx8MFrIEtUj2JTZL9m47nLvhwAFmyR4GCWOCWgzOAF3FC9IC9Is00C+JBLXYA7Uw/p1zOHd+wwHm30RpMeBhbGPmbTic23DgABtxtkicYWyWljmWDvTLwTbLHgMi/MLfw/7w45sa69z5Mw4fvvGjoo5wiCHZdxCo2IB49SD7SDB+FIyCUTAKRhYAAPN2PyHwhnKrAAAAAElFTkSuQmCC","orcid":"","institution":"Universitas Mochammad Sroedji Jember","correspondingAuthor":true,"prefix":"","firstName":"Erica","middleName":"Ferry Sukma","lastName":"Sitepu","suffix":""},{"id":416572181,"identity":"4dca8d8b-89e3-44b0-a9bf-1cce8363661c","order_by":1,"name":"Maria Magdalena Lily Bina","email":"","orcid":"","institution":"Universitas Mochammad Sroedji Jember","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Magdalena Lily","lastName":"Bina","suffix":""},{"id":416572182,"identity":"8e0a0c65-d406-42e4-8f92-6127d82fb124","order_by":2,"name":"Nyoman Soekarini","email":"","orcid":"","institution":"Universitas Mochammad Sroedji Jember","correspondingAuthor":false,"prefix":"","firstName":"Nyoman","middleName":"","lastName":"Soekarini","suffix":""}],"badges":[],"createdAt":"2025-02-05 14:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5966496/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5966496/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76437628,"identity":"e2073825-c36e-423c-81e4-4f07f21388e5","added_by":"auto","created_at":"2025-02-17 07:50:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88763,"visible":true,"origin":"","legend":"\u003cp\u003eHeterogeneity Analysis Results\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5966496/v1/04c79c0ef5b0510222f35803.png"},{"id":95193797,"identity":"ef2a2445-f3fb-4e38-9fe6-91cad1ee30c5","added_by":"auto","created_at":"2025-11-05 10:54:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":693510,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5966496/v1/0adcd41c-3cec-4b03-badc-2889dc917cad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Can Climate Change Adaptation and Energy Efficiency Drive Economic Growth in Indonesia?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eEnvironmental issues have emerged as a growing global concern due to their profound and far-reaching impact on economic growth (K. Gao \u0026amp; Yuan, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Environmental degradation, including deforestation, air pollution, and the overexploitation of natural resources, has the potential to diminish economic productivity, intensify poverty, and exacerbate social inequality. Improper management of natural resources can lead to conflict and economic instability, particularly in developing countries. Consequently, sustainable environmental management is not only essential for preserving ecosystems but also serves as a crucial driver of inclusive economic growth (Asongu \u0026amp; Odhiambo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ge et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimate change has become a widespread problem and a threat to human welfare, development and the environment (Farajzadeh et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Petrović, (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) confirms that climate change can significantly impact economic growth. The rise in carbon dioxide emissions intensifies risks to human survival and undermines environmental sustainability over the long term (Khan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Skytt et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The rise in global temperatures, the increasing frequency of natural disasters, and shifting weather patterns pose significant threats to food production and energy availability (Bolan et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Duchenne-Moutien \u0026amp; Neetoo, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These challenges can hinder economic growth, particularly in countries highly vulnerable to the impacts of climate change, such as Indonesia. Consequently, climate change mitigation has become an urgent priority to ensure sustainable economic development in the face of escalating environmental threats (Ferreira et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Van Hoa \u0026amp; Limskul, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEnergy efficiency is one approach that can have a positive impact on economic growth (Adom et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lin \u0026amp; Zhou, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Optimizing energy use, energy efficiency can reduce production costs, increase industrial competitiveness, and open up new investment opportunities (Bonilla-Campos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ibn Batouta et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, energy efficiency can reduce dependence on fossil fuels, which not only supports the transition to a green economy but also reduces carbon emissions, helping Indonesia contribute to climate change mitigation.\u003c/p\u003e \u003cp\u003eIn the midst of efforts to mitigate climate change, energy efficiency is a strategic alternative that can encourage sustainable economic growth. Energy efficiency not only functions as a mitigation measure, but is also able to create synergy between environmental protection and economic development (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lin \u0026amp; Zhou, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By energy efficiency, Indonesia can harness the potential of technological innovation and cost savings to create a more inclusive and environmentally friendly economy.\u003c/p\u003e \u003cp\u003eIndonesia faces significant economic challenges due to climate change, with potential losses stemming from both direct impacts and the costs associated with mitigation strategies. The country's efforts to improve energy efficiency and reduce carbon emissions are crucial in addressing these challenges. Indonesia's commitment to reducing greenhouse gas emissions by 29% by 2030 is expected to result in a GDP loss of around 1.7% compared to the business-as-usual (BAU) scenario. The agriculture sector is projected to experience the most significant impact, with a 13.4% reduction in GDP by 2030, while the energy sector might see a 3.5% increase under mitigation actions (Malahayati \u0026amp; Masui, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Compared to other Southeast Asian countries, Indonesia's energy intensity has been more pronounced, with a steady increase until 1999 and a subsequent decline. This trend reflects a shift towards more energy-intensive industries, which is a common pattern in the region.\u003c/p\u003e \u003cp\u003eClimate change adaptation and energy efficiency have the potential to drive economic growth in Indonesia. Furthermore, advancements in building energy efficiency can significantly reduce carbon emissions, thereby supporting economic growth through sustainable practices. These strategies align with Indonesia's climate goals and have the potential to stimulate economic activities. While addressing environmental concerns, these measures contribute to a more environmentally friendly development.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eEndogenous growth theory explains that economic growth does not only depend on external factors, but is also influenced by internal factors in an economic system. These factors include technological innovation, human capital accumulation, investment in research and development (R\u0026amp;D), and government policies that support sustainability (Blackburn et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Sequeira, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In this theory, long-term growth results from endogenous processes, such as experience-based learning (learning by doing) and the spread of knowledge, which can increase productivity without limits\u003c/p\u003e \u003cp\u003eIn an environmental context, endogenous growth theory is relevant to explain how investment in environmentally friendly technology and energy efficiency can encourage economic growth while reducing negative impacts on the environment (Elbasha \u0026amp; Roe, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Oueslati, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). For example, technological innovation in the renewable energy or energy efficiency sector can reduce production costs, increase competitiveness, and create new markets that support the transition to a green economy. Thus, internal factors such as government policies that support research and adoption of low-carbon technologies play an important role in creating sustainable economic growth.\u003c/p\u003e \u003cp\u003eThis theory highlights the critical role of education and the enhancement of workforce quality as essential components of human capital. A workforce equipped to adopt new technologies and foster innovation plays a pivotal role in driving economic progress. Over time, investments in both human capital and technology generate spillover effects, contributing not only to the acceleration of domestic economic growth but also addressing broader global challenges, including climate change.\u003c/p\u003e \u003cp\u003eBy incorporating climate change adaptation and energy efficiency into growth strategies, endogenous growth theory offers a robust framework for achieving a harmonious balance between economic development and environmental sustainability. This approach emphasizes that sustainable development can be pursued through investments that promote technological advancement and human capital, ultimately fostering long-term economic resilience and environmental stewardship.\u003c/p\u003e \u003cp\u003eResearch conducted(Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) discusses the role of energy efficiency, natural resource management, financial risk, and technological innovation in driving sustainable economic growth in BRICS countries (Brazil, Russia, India, China, and South Africa). The results show that energy efficiency and technological innovation significantly contribute to long-term economic growth in BRICS countries. However, inefficient management of natural resources can have negative impacts. On the other hand,Lin \u0026amp; Zhou, (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) explore the relationship between energy efficiency (EFF) and the quality of economic growth in China using provincial panel data from 2000 to 2017. Energy efficiency significantly improves the quality of economic growth in the eastern region, while in the central and western regions, it reduces the quality of growth. These findings underscore the existence of strong regional differences, driven by different levels of technology, economic structure and resource utilization in each region.Adom et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) discusses the relationship between energy efficiency and economic growth in 51 African countries, considering the moderating role of income inequality. This research uses the stochastic frontier analysis (SFA) method to measure energy efficiency and the Generalized Method of Moments (GMM) approach to analyze panel data from 1991 to 2017. The results show that improvements in energy efficiency directly trigger economic growth, but the impact decreases in countries -countries with high-income inequality.\u003c/p\u003e \u003cp\u003eOn the other hand, research conducted by Petrović, (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explained at the current level of climate change, its impact on global economic growth still tends to be positive on average, but mitigation measures are still needed to avoid negative consequences in the future. Farajzadeh et al., (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) analyzes the impact of climate change on economic growth in Asian countries, one of the region\u0026rsquo;s most vulnerable to climate change. Climate change significantly affects output per worker through three channels: reduced productivity, increased capital depreciation rates, and direct impacts on output levels. Lu et al., (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) discusses the impact of climate change on sustainable economic development with a focus on the Nanjing region, China. Increased rainfall and decreased temperatures can reduce economic impacts, while greater temperature variations have negative effects on capital investment and economic development\u003c/p\u003e"},{"header":"3. Methods and Data Analysis","content":"\u003cp\u003eThe objective of this investigation is to analyze the influence of Climate Change Adaptation and Energy Efficiency on the promotion of economic growth. This analysis will describe the data, variables, and methodologies used to attain this aim. The findings are designed to provide insights into how these components can promote sustainable economic development.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis research utilizes secondary data, specifically in the form of panel data. The panel data spans a period from 2014 to 2023. It covers 13 provinces across Indonesia, providing a comprehensive overview of the relevant trends. The data includes various economic and environmental indicators, crucial for the analysis of the study. The primary data source is the Indonesian Central Statistics Agency (BPS). BPS is a reputable institution responsible for collecting and disseminating official statistics in Indonesia. This data enables the research to examine regional variations and patterns over the study period.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Explained Variable: Economic Growth\u003c/h2\u003e \u003cp\u003eEconomic growth reflects the overall level of economic activity within a specific region. It is often used as a key indicator to assess the economic health and development of a region. Regional economic growth is commonly measured by tracking changes in output over a given period of time. This measurement is typically expressed through the Gross Regional Domestic Product (GRDP), which reflects the total value of goods and services produced in a region. GRDP serves as a comprehensive indicator of economic performance at the regional level. For this research, GRDP is used to assess economic growth in 13 provinces across Indonesia. By examining the GRDP data, the study aims to understand the economic dynamics and growth patterns in these regions over time.\u003c/p\u003e \u003cp\u003eEconomic growth is an important aspect that is influenced by various factors. Research conducted byLu et al., (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrations that climate change not only damages the environment but also has long-term impacts on the economic and social development of a region. Besides that,Farajzadeh et al., (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) confirm the existence of a significant relationship between climate change and economic growth. On the other hand, energy efficiency has also been proven to influence economic growth, as stated in research (Adom et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lin \u0026amp; Zhou, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These findings are important for providing empirical insight into the relationship between environmental sustainability and economic growth, as well as providing a basis for formulating policies that support sustainable economic growth\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Explanatory Variable\u003c/h2\u003e \u003cp\u003e \u003cb\u003eClimate Change\u003c/b\u003e \u003c/p\u003e \u003cp\u003eClimate change is defined as a shift in average temperature and rainfall that occurs over time, which is measured using a statistical approach (Fan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kahn et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; X. Wu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Climate change indicators use concepts Wu et al., (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with the main indicators in the form of deviation levels of national temperature and rainfall from historical averages as a basis for identifying changes in climate conditions. The sample data used covers the period 2014\u0026ndash;2023 in 13 provinces in Indonesia. However, to measure climate change, historical data on temperature and rainfall from 2000\u0026ndash;2023 for the 13 provinces is used.\u003c/p\u003e \u003cp\u003eThe first step is to measure temperature and rainfall anomalies by subtracting sample data from historical data. The anomaly value is then used to calculate the variance, which is then normalized to support further analysis.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{k}^{*}=\\frac{k-\\:{k}_{min}}{{k}_{max}-{k}_{min}}$$\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\\(\\:{k}^{\\text{*}}\\)\u003c/span\u003e\u003c/span\u003e describes data normalization of data, k is the variance of temperature and rainfall. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{k}_{max}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{k}_{min}\\)\u003c/span\u003e\u003c/span\u003e each shows the maximum and minimum values ​​of the variance of temperature and precipitation.\u003c/p\u003e \u003cp\u003eThe second stage involves measuring climate change (CC) by utilizing the results of normalized variance of temperature (TEMP) and precipitation (PCPT), which are then multiplied by weights that have been determined based on data for the 2014 to 2023 period\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{CC}_{it}={\\omega\\:}_{1}{TEMP}_{it}+\\:{\\omega\\:}_{2}{PCPT}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003econstant \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\omega\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\omega\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e functions as a weight that describes the impact of temperature and rainfall fluctuations on the financial industry sector. Determination of weight \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\omega\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\omega\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e This was done by calculating the correlation between normalized deviations in temperature and rainfall and the growth rate of the financial industry. This financial sector growth is represented through changes in the financial sector's contribution to economic growth.\u003c/p\u003e \u003cp\u003eClimate change has a significant impact on economic growth, both directly and indirectly. Climate change exhibits an inverted U-shaped effect on economic growth in tropical rainforest and dry climate zones, negatively impacting agriculture and services sectors (Zhao \u0026amp; Liu, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Addressing climate change through mitigation and adaptation strategies is crucial to minimizing these adverse impacts and fostering sustainable economic growth\u003c/p\u003e \u003cp\u003e \u003cb\u003eEnergy Efficiency\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHigh pressure to reduce emissions and solve energy problems demands the implementation of more optimal energy efficiency. Green Total Factor Energy Efficiency (GTFEE) is a strategic approach designed to address the energy crisis and climate change while ensuring the achievement of economic output targets (Wu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). GTFEE is defined as the comparison between the expected energy value and the actual value under conditions of endogenous pollution (Wu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This ratio reflects the achievement of optimal economic benefits with minimal levels of environmental pollution, which is obtained through a comprehensive analysis of input factors such as capital, labour and energy in the production process (Wu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the input side, increasing GTFEE contributes to reducing energy waste and supporting economic growth, thereby reducing pressure on energy supplies (Hancevic \u0026amp; Sandoval, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Meanwhile, on the output side, increasing GTFEE can reduce pollutant emissions and help achieve economic output targets (Gao \u0026amp; Yuan, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which can effectively mitigate the impact of climate change on economic development.\u003c/p\u003e \u003cp\u003eMeasurement Green Total Factor Energy Efficiency (GTFEE) is carried out using the model Super-Efficiency Slacks-Based Data Envelopment Analysis (SBM-DEA) adopted from research (Gao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The SBM-DEA model was chosen because of its ability to overcome the limitations of conventional DEA methods, especially in analysing actual input and output slack and measuring efficiency under unexpected output constraints (Lee \u0026amp; Kim, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The SBM-DEA Model formulation is as follows\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:minimize\\:e=\\frac{1-\\frac{1}{m}\\sum\\:_{i=1}^{m}\\frac{{s}_{i}^{-}}{{x}_{io}}}{1+\\frac{1}{s}\\sum\\:_{r=1}^{s}\\frac{{s}_{r}^{+}}{{y}_{r0}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSubject to\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{x}_{0}=X\\lambda\\:+{s}^{-}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{y}_{0}=Y\\lambda\\:-{s}^{+}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{s}^{-}\\ge\\:0,{s}^{+}\\ge\\:0,\\lambda\\:\\ge\\:0$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere e is the evaluated DMU efficiency, m and s is the sum of input and output. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}^{-}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}^{+}\\)\u003c/span\u003e\u003c/span\u003e the representation of slacks in input and output.\u003c/p\u003e \u003cp\u003eThe input factors used in this research include capital, labour and energy. Regional fixed capital is measured using Gross Fixed Capital Formation (GFCF), while labour is represented by the number of workers in each province. Since provincial level energy consumption data is not available, electricity consumption is used as an energy indicator. Output factors consist of expected output and unexpected output. Expected output is represented through regional economic growth indicators, while unexpected output is measured using the total waste produced.\u003c/p\u003e \u003cp\u003eThe impact of energy efficiency on economic growth is very significant, because it can reduce operational costs in various sectors, increase industrial competitiveness, and encourage investment in environmentally friendly technologies. Optimizing energy use in reducing inefficient energy consumption, thus encouraging a sustainable economy (Adha et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Other Variable\u003c/h2\u003e \u003cp\u003eOther variables used in this research are inflation, primary industrial sector and green infrastructure. Inflation (INF) is presented as consumer purchasing power which can influence economic growth (Tillaguango et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The primary industry variable is represented by the agriculture, livestock, hunting and agricultural services sectors can affect economic growth. Climate change and energy efficiency levels can affect primary sector productivity. On the other hand, research conducted by Soltani et al., (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explains that climate change can affect energy efficiency in the agricultural sector. Green infrastructure is government spending to mitigate climate change (Fan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Investment in green infrastructure to encourage economic growth\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Models\u003c/h2\u003e \u003cp\u003eThis research model is a modification of research conducted by Adom et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Farajzadeh et al., (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Lu et al., (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Petrović, (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The model is built as follows\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{it}=\\:{a}_{0}+{a}_{1}{CC}_{it}+{a}_{2}{EE}_{it}+{a}_{3}{pi}_{it}+{a}_{4}{gi}_{it}+{a}_{5}{inf}_{it}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEquation (\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) explains the relationship between climate change, energy efficiency, primary industry, green investment and inflation on economic growth. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{it}\\)\u003c/span\u003e\u003c/span\u003e is economic growth in region i at time t. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CC}_{it}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{EE}_{it}\\)\u003c/span\u003e\u003c/span\u003e is climate change and energy efficiency in region i at time t. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{pi}_{it}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{gi}_{it}\\)\u003c/span\u003e\u003c/span\u003e is an indicator of the primary sector and green investment in region i at time t. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{inf}_{it}\\)\u003c/span\u003e\u003c/span\u003e is inflation in region i at time t. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e defined as a random disturbance term.\u003c/p\u003e \u003cp\u003eThe model in this study was estimated using the method of Mixed-Effect Maximum Likelihood (ML) Regression, which is designed to simultaneously capture fixed effects and random effects in panel data analysis. This approach allows controlling variations between individuals or groups that are not directly observed while producing more accurate and efficient parameter estimates. This method is also very suitable for use on data with a hierarchical structure or in situations where there is heterogeneity between observation units.\u003c/p\u003e \u003cp\u003eIn addition, this research also applies heterogeneity analysis to evaluate the impact of climate change and energy efficiency on economic growth under certain conditions. The particular conditions in question involve differences in regional characteristics, namely areas dominated by the Primary Industrial Sector (PI) and the presence of Green Infrastructure (GI). This approach aims to provide a more comprehensive understanding of the relationship between environmental factors and economic growth in various regional contexts.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Results\u003c/h2\u003e \u003cp\u003eBased on the results of the analysis using the method of Mixed-Effect Maximum Likelihood (ML) Regression explained that climate change and energy efficiency influence economic growth. On the other hand, primary variables and green investment affect economic growth, but inflation does not affect economic growth. The results of the analysis using the method of Mixed-Effect Maximum Likelihood (ML) Regression can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis results in Mixed-Effect Maximum Likelihood (ML) Regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\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\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ez-statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,016*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,019*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,00*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLR test vs. linear model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: * significant α\u0026thinsp;=\u0026thinsp;5%, ** significant α\u0026thinsp;=\u0026thinsp;10%\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that climate change has a coefficient of -0.001 and a z-statistic value of -2.77 and a p-value of 0.0016\u0026thinsp;\u0026lt;\u0026thinsp;α\u0026thinsp;=\u0026thinsp;5%. This indicates that the relationship between climate change and economic growth is negative and significant at the 95% confidence level. An increase in the intensity of climate change is statistically associated with a decrease in economic growth, although the effect tends to be small. Lu et al., (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Petrović, (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explains that climate change, such as increasing temperatures and changes in rainfall, has a significant impact on economic growth. Changes in temperature and rainfall can have an impact productivity of natural resources, especially in sectors that are highly dependent on climate such as agriculture, fisheries and energy, which ultimately slows down economic growth (Farajzadeh et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Long term, climate change may affect economic structure by forcing countries to shift focus from climate-based sectors to sectors that are more resilient to environmental change. Nevertheless, this transition requires time and investment, which often limits growth in the short term (Farajzadeh et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)​. Endogenous Growth Theory explains that economic growth is influenced by various internal factors. Climate change on key sectors such as agriculture and industry can reduce the level of investment in technology and human resources, which in turn can hamper long-term economic growth.\u003c/p\u003e \u003cp\u003eEnergy efficiency shows a positive coefficient of 1.251 with a z-statistic value of 2.91 and a p-value of 0.004\u0026thinsp;\u0026lt;\u0026thinsp;α\u0026thinsp;=\u0026thinsp;5%. The significant positive relationship between energy efficiency and economic growth shows that increasing energy efficiency makes a positive contribution to economic growth. In other words, efficiency in energy use plays an important role in supporting productivity and encouraging sustainable economic growth. Increasing energy efficiency has been proven to encourage sustainable economic growth by reducing carbon emissions and increasing energy productivity​ (Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Adom et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Energy efficiency plays a crucial role in reducing the wastage of natural resources by optimizing their use and extending their useful life. By minimizing energy consumption, resources are conserved, leading to lower environmental impact. The resulting energy savings can be redirected towards other economic activities, fostering overall economic productivity and sustainability. Endogenous growth theory is relevant to explain how investment in environmentally friendly technology and energy efficiency can encourage economic growth while reducing negative impacts on the environment (Elbasha \u0026amp; Roe, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Oueslati, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe primary industrial sector also shows a positive relationship with economic growth, with a coefficient of 0.331, a z-statistic value of 2.36, and a p-value of 0.019\u0026thinsp;\u0026lt;\u0026thinsp;α\u0026thinsp;=\u0026thinsp;5%. The significant positive relationship between sectors of Primary industry and economic growth shows that the dominance of the primary industrial sector in a region makes a positive contribution to economic growth. The Primary Industry Development Level, which is measured by the development of primary industrial sectors, such as agriculture, forestry and fisheries, influences economic growth (Elzaki, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Grabowski \u0026amp; Self, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the other hand, green investment shows a coefficient of 0.209 with a z-statistic value of 6.48 and a p-value of 0.000\u0026thinsp;\u0026lt;\u0026thinsp;α\u0026thinsp;=\u0026thinsp;5%. Increasing investment in green infrastructure consistently has a positive impact on economic growth. Green infrastructure development is one of the key factors in supporting sustainable economic growth (Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Green infrastructure is government spending to mitigate climate change(Fan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe analysis reveals that inflation has a positive coefficient of 0.002, with a z-statistic value of 0.43 and a p-value of 0.668, which is greater than the significance level of α\u0026thinsp;=\u0026thinsp;5%. This indicates that the relationship between inflation and economic growth is statistically insignificant, suggesting that inflation does not exert a substantial influence on economic growth in this context. Furthermore, the likelihood ratio (LR) test comparing the mixed-effects model to the linear regression model yields a probability value of 0.000, affirming that the mixed-effects model is more appropriate for this data than the traditional linear regression model. These results underscore the importance of incorporating both fixed and random effects in the analysis, which enables more robust and precise estimates for understanding the complex dynamics of economic growth.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of heterogeneity analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePrimary Industry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGreen Investment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,019*\u003c/p\u003e \u003cp\u003e[-6,83]\u003c/p\u003e \u003cp\u003e(0,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,000*\u003c/p\u003e \u003cp\u003e[4,96]\u003c/p\u003e \u003cp\u003e(0,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,004*\u003c/p\u003e \u003cp\u003e[-2,29]\u003c/p\u003e \u003cp\u003e(0,020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,000*\u003c/p\u003e \u003cp\u003e[2,92]\u003c/p\u003e \u003cp\u003e(0,005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,060*\u003c/p\u003e \u003cp\u003e[2,72]\u003c/p\u003e \u003cp\u003e(0,008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,530\u003c/p\u003e \u003cp\u003e[-1,16]\u003c/p\u003e \u003cp\u003e(0,252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,879*\u003c/p\u003e \u003cp\u003e[2,64]\u003c/p\u003e \u003cp\u003e(0,015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,076*\u003c/p\u003e \u003cp\u003e[2,32]\u003c/p\u003e \u003cp\u003e(0,024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,019\u003c/p\u003e \u003cp\u003e[-1,44]\u003c/p\u003e \u003cp\u003e(0,156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,016\u003c/p\u003e \u003cp\u003e[0,37]\u003c/p\u003e \u003cp\u003e(0,713)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,002\u003c/p\u003e \u003cp\u003e[0,04]\u003c/p\u003e \u003cp\u003e(0,968)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,005\u003c/p\u003e \u003cp\u003e[-0,07]\u003c/p\u003e \u003cp\u003e(0,941)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: * significant α\u0026thinsp;=\u0026thinsp;5%, ** significant α\u0026thinsp;=\u0026thinsp;10%\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e[\u0026hellip;]\u0026thinsp;=\u0026thinsp;z-statistic (\u0026hellip;)\u0026thinsp;=\u0026thinsp;Prob.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe heterogeneity analysis presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveals that the impact of climate change on economic growth is contingent on the level of the primary industrial sector and green investment in a region. In regions with a low primary industrial sector, climate change exhibits a significant negative relationship with economic growth, as evidenced by a coefficient of -0.019 (z-statistic \u0026minus;\u0026thinsp;6.83, p\u0026thinsp;=\u0026thinsp;0.000). This indicates that the increasing effects of climate change substantially hinder economic growth in these regions, likely due to their limited industrial diversification and greater reliance on sectors vulnerable to environmental disruptions.\u003c/p\u003e \u003cp\u003eIn contrast, regions with a high primary industrial sector display a much smaller, yet statistically significant, negative effect of climate change, with a coefficient of -0.000 (z-statistic 4.96, p\u0026thinsp;=\u0026thinsp;0.000). This suggests that these regions may exhibit greater resilience to climate-related impacts, possibly owing to the robustness of the primary industries, which are often more adaptable to environmental fluctuations.\u003c/p\u003e \u003cp\u003eRegarding green investment, climate change has a significant negative effect on economic growth at both low (-0.004, z-statistic \u0026minus;\u0026thinsp;2.29, p\u0026thinsp;=\u0026thinsp;0.020) and high (-0.000, z-statistic 2.92, p\u0026thinsp;=\u0026thinsp;0.005) levels of green investment. However, the negative impact of climate change is less pronounced in regions with higher levels of green investment, underscoring the mitigating role that sustainable investment can play in reducing the adverse effects of climate change on economic performance.\u003c/p\u003e \u003cp\u003eThe impact of energy efficiency on economic growth varies significantly based on the regional characteristics of the primary industrial sector and the level of green investment. In regions with a low primary industrial sector, energy efficiency exhibits a significant positive influence, with a coefficient of 1.060 (z-statistic 2.72, p\u0026thinsp;=\u0026thinsp;0.008). This suggests that improving energy efficiency in these areas plays a crucial role in boosting economic growth. The positive relationship indicates that regions with less reliance on primary industries can capitalize on energy efficiency improvements to enhance productivity and foster sustainable economic development. The significant effect in low primary industrial regions highlights the potential for energy efficiency to serve as a key driver of economic transformation in these areas.\u003c/p\u003e \u003cp\u003eIn contrast, regions with a high primary industrial sector do not show a significant relationship between energy efficiency and economic growth. The coefficient for these regions is -0.530 (z-statistic \u0026minus;\u0026thinsp;1.16, p\u0026thinsp;=\u0026thinsp;0.252), indicating an insignificant negative impact. This lack of significance may be attributed to the inherent rigidity of primary industrial sectors, which are often less adaptable to the adoption of energy-efficient technologies. These sectors may face challenges in integrating such innovations due to their reliance on traditional, resource-intensive production processes.\u003c/p\u003e \u003cp\u003eRegarding green investment, energy efficiency demonstrates a significant positive relationship at both low (0.879, z-statistic 2.64, p\u0026thinsp;=\u0026thinsp;0.015) and high (1.076, z-statistic 2.32, p\u0026thinsp;=\u0026thinsp;0.024) investment levels. These results underscore the importance of energy efficiency as a critical factor supporting economic growth in regions that prioritize green investment. Enhanced energy efficiency, coupled with green investment, facilitates the transition to a more sustainable and resilient economy, benefiting regions regardless of their industrial composition.\u003c/p\u003e \u003cp\u003eThe analysis reveals that inflation does not significantly influence economic growth across different categories of primary industrial sectors or green investment. In regions with a low primary industrial sector, the coefficient for inflation is -0.019 (z-statistic \u0026minus;\u0026thinsp;1.44, p\u0026thinsp;=\u0026thinsp;0.156), indicating a weak negative relationship, but this result is not statistically significant. Similarly, in regions with a high primary industrial sector, the coefficient is 0.016 (z-statistic 0.37, p\u0026thinsp;=\u0026thinsp;0.713), suggesting a negligible positive effect, which is also statistically insignificant. Regarding green investment, the relationship between inflation and economic growth remains insignificant at both low (0.002, z-statistic 0.04, p\u0026thinsp;=\u0026thinsp;0.968) and high (-0.005, z-statistic \u0026minus;\u0026thinsp;0.07, p\u0026thinsp;=\u0026thinsp;0.941) investment levels. These findings suggest that inflation does not play a significant role in driving economic growth within these specific regional categories.\u003c/p\u003e \u003cp\u003eThe results of the heterogeneity analysis, as depicted in Fig.\u0026nbsp;1, reveal varying impacts of climate change, energy efficiency, and inflation on economic growth across different quantiles. Specifically, Fig.\u0026nbsp;1(a) illustrates that climate change consistently exerts a negative effect on economic growth at all quantiles, but this impact intensifies in regions with more advanced economic conditions. The analysis shows a reduction in the coefficient values at higher quantiles, suggesting that the adverse effects of climate change are more pronounced in economically developed regions. This may be attributed to the increased reliance on infrastructure and sectors that are more vulnerable to environmental changes, such as urbanized areas and industries with high carbon footprints. Consequently, the results highlight the compounded challenges climate change poses to economically developed regions.\u003c/p\u003e \u003cp\u003eFigure 1(b) demonstrates a positive relationship between energy efficiency and economic growth, with the strength of this relationship increasing at higher quantiles. This pattern suggests that regions with more favorable economic conditions experience greater benefits from improvements in energy efficiency. The enhanced positive effect in economically developed regions may be due to their capacity to implement advanced technologies and optimize resource management practices, which are crucial for improving productivity and fostering sustainable growth. Regions with higher investment capacity are better equipped to adopt energy-efficient technologies and infrastructures, thus driving economic growth more effectively. These findings underscore the pivotal role of energy efficiency as a key driver of economic development, particularly in regions with the financial resources to support green technologies and innovations.\u003c/p\u003e \u003cp\u003eFigure 1(c) reveals a non-linear relationship between inflation and economic growth, highlighting varying impacts across different quantiles. At low to middle quantiles, inflation exhibits a weak positive effect on economic growth, suggesting that moderate inflation may be associated with economic expansion in less developed regions. However, as the quantiles increase, the relationship shifts, with inflation turning negative and its impact intensifying in regions with higher economic conditions. This indicates that economically developed regions are more susceptible to the detrimental effects of inflation, possibly due to factors such as higher costs of living, wage pressures, and financial market volatility. The results emphasize that inflation's influence on economic growth is contingent upon the specific economic context and development level of a region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Discussion\u003c/h2\u003e \u003cp\u003eThis research demonstrates that climate change, energy efficiency, and inflation significantly influence economic growth, with the impact varying depending on regional structural conditions such as the dominance of the primary industrial sector and the level of green investment. These findings underscore the complex interplay between environmental factors and regional economic dynamics. The influence of climate change, in particular, is shown to have a notable negative relationship with overall economic growth, suggesting that the increasing severity of climate change can impede economic progress. This effect is especially pronounced in regions where the primary industrial sector is less dominant, where vulnerability to climate-related disruptions is higher.\u003c/p\u003e \u003cp\u003eHowever, the negative impact of climate change on economic growth tends to be smaller in regions with a significant primary industrial sector. This may be attributed to the relative resilience of industries within these sectors, which are better equipped to withstand the adverse effects of climate change. As such, regions with a dominant primary industrial base may possess certain structural advantages that mitigate the immediate negative consequences of climate-induced disruptions. Nonetheless, the findings indicate that even in these regions, the long-term effects of climate change could pose a challenge to sustained economic growth if not adequately addressed.\u003c/p\u003e \u003cp\u003eEnergy efficiency, on the other hand, has been identified as a key factor supporting economic growth. The research highlights that energy efficiency exerts a significant positive influence on economic performance, particularly in regions with low primary industrial sectors and high levels of green investment. This suggests that regions with advanced energy efficiency measures and a strong focus on green investment are better positioned to optimize resource use, leading to enhanced economic growth. The positive relationship between energy efficiency and economic growth points to the potential for energy innovations to act as a catalyst for regional development.\u003c/p\u003e \u003cp\u003eIn contrast, the impact of energy efficiency is less pronounced in regions dominated by the primary industrial sector. This discrepancy may be attributed to the lower adaptability of industries within these sectors to energy-saving technologies and innovations. The rigidities inherent in primary industries, such as agriculture and extractive sectors, make them less flexible in adopting energy-efficient practices. As a result, while energy efficiency contributes to economic growth in more diversified or green investment-focused regions, its potential in primary industry-dominated regions remains limited unless there is substantial transformation within these sectors.\u003c/p\u003e \u003cp\u003eBased on the analysis, several policy recommendations emerge. First, it is imperative for the government to strengthen climate change mitigation strategies, particularly through reducing carbon emissions and protecting vulnerable sectors. Policies should focus on the development of green investments, such as environmentally friendly technologies and infrastructure capable of adapting to climate change. These measures should be prioritized in regions with low primary industrial sectors, where the impact of climate change is more pronounced. This approach will help mitigate the negative effects of climate change on economic growth, enabling regions to pursue more sustainable development trajectories.\u003c/p\u003e \u003cp\u003eMoreover, promoting energy efficiency must be a central focus of government policy. Incentives for both the industrial sector and households to adopt energy-saving technologies, such as subsidies for energy-efficient devices or tax credits for renewable energy investments, are essential. Additionally, increasing investment in green infrastructure, particularly in low-emission public transport, renewable energy generation, and efficient waste management systems, will be crucial in supporting long-term sustainable development. These investments will not only improve energy efficiency but also contribute to broader environmental goals, fostering a more resilient and sustainable economy.\u003c/p\u003e \u003cp\u003eIn regions where the primary industrial sector dominates, the focus should shift toward economic diversification and the integration of green technologies. Policies should prioritize workforce training, incentives for the adoption of low-carbon technologies, and the modernization of production processes to reduce the environmental impact of these industries. While inflation did not show a significant direct influence on the results of this study, maintaining inflation stability remains critical. Prudent monetary policy and the management of basic commodity prices are essential to support consumer purchasing power and ensure continued investment. Furthermore, increased regional collaboration is necessary, enabling regions with low green investment to benefit from the experiences and technological advancements of regions with higher levels of green investment. This approach, encompassing technology transfer, best practices sharing, and public-private partnerships, will promote inclusive and sustainable economic growth and enhance regional resilience to climate change.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eClimate change and energy efficiency significantly influence economic growth, although the extent of their impact varies according to regional characteristics. The findings indicate that climate change negatively affects overall economic growth, particularly in regions with a low primary industrial sector, where the capacity to withstand climate-related disruptions is limited. This relationship suggests that regions with a more diversified or lower reliance on primary industries face greater vulnerabilities to the adverse effects of climate change. Conversely, the negative impact of climate change is less pronounced in regions dominated by primary industrial sectors. This may be attributed to the inherent resilience of such sectors, which can better adapt to climate change due to their operational structure and existing infrastructure.\u003c/p\u003e \u003cp\u003eEnergy efficiency is identified as a critical driver of economic growth, especially in more developed regions with substantial green investment. These regions demonstrate a higher capacity to leverage energy efficiency measures to enhance productivity and foster the transition to a green economy. However, the influence of energy efficiency is less significant in regions with a strong primary industrial base, suggesting that these sectors require modernization and greater adoption of energy-saving technologies. Based on these findings, the research recommends the implementation of climate change mitigation policies focused on green investment, renewable energy subsidies, and economic diversification. These strategies aim to promote inclusive and sustainable economic growth while strengthening resilience to environmental challenges.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, EFSS; methodology, MMLB; software, MMLB; validation, EFSS; formal analysis, NS; data curation, MMLB; writing original draft preparation, NS; writing review and editing, EFSS. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdha R, Hong C-Y, Yang S-F, Muzayyanah S (2024) Re-Unveiling the energy efficiency impact: Paving the way for sustainable growth in ASEAN countries. 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Sustain (Switzerland) 15(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su15097136\u003c/span\u003e\u003cspan address=\"10.3390/su15097136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate Change, Energy Efficiency, Economic Growth, Mixed-Effect Maximum Likelihood","lastPublishedDoi":"10.21203/rs.3.rs-5966496/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5966496/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change and energy efficiency contribute to economic growth. This study aims to examine the impact of climate change and energy efficiency on economic growth across various regions in Indonesia, considering the influence of primary industrial sectors and green investment. The data used is panel data from 13 provinces during the 2014–2023 period. The method used is Mixed-Effect Maximum Likelihood Regression. The research results show that climate change has a significant negative impact on economic growth, especially in regions with a low primary industrial sector. Conversely, energy efficiency demonstrates a significant positive impact, particularly in regions characterized by substantial green investment and a dominant non-primary sector. However, energy efficiency does not exhibit a significant impact in regions with a highly developed primary industrial sector, highlighting the need for modernization within this sector. Meanwhile, inflation was found to have no significant impact on economic growth across all regional categories. Climate change mitigation strategies, including reducing carbon emissions, enhancing energy efficiency, and increasing investment in green infrastructure, are essential for fostering inclusive and sustainable economic growth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJell Classification: \u003c/strong\u003eR11, P28, F43,C32\u003c/p\u003e","manuscriptTitle":"Can Climate Change Adaptation and Energy Efficiency Drive Economic Growth in Indonesia?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-17 07:50:33","doi":"10.21203/rs.3.rs-5966496/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bbab53af-760a-453f-bd9c-58005044a26a","owner":[],"postedDate":"February 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44414245,"name":"Business and commerce/Economics"},{"id":44414246,"name":"Business and commerce/Finance"},{"id":44414247,"name":"Social science/Environmental studies"}],"tags":[],"updatedAt":"2025-11-05T10:54:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-17 07:50:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5966496","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5966496","identity":"rs-5966496","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}