Fully Modified Ordinary Least Squares Research Articles

Priority and non-priority sector GNPAS in Indian commercial banks: a comparison between sub-sectors

Purpose This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private and foreign sector banks. Design/methodology/approach The Reserve Bank of India's database on the Indian economy is used to retrieve data over 13 years (2008–2021). Public sector (12), private sector (22) and foreign sector (44) banks are represented in the sample. Two-way ANOVA, multiple regression and panel regression statistical techniques are used in SPSS and EViews to examine the data. Further, the results are also validated by using robustness testing by applying the fully modified ordinary least square (FMOLS) and dynamic least square (DOLS) regression. Findings The results showed that, for private and foreign banks, the non-priority sector makes up the majority of the total gross non-performing assets, although both the priority and non-priority sectors are substantial for public sector banks. The largest contributors to the total gross non-performing assets in public, private and foreign banks are industries, agriculture and micro and small businesses. The FMOLS displays robustness results that are qualitatively similar to the baseline result. Practical implications Based on the study's findings about the patterns of non-performing assets originating from these specific industries, banks might improve the way in which these advanced loans are managed. Originality/value There has not been much research done on the subject of sub-sector-specific non-performing assets and how they affect total gross non-performing assets across the three sector banks. The study's primary focus will be on the issue of non-performing assets in the priority’s and non-priority’s sub-sectors, namely, agricultural, micro and small businesses, food credit, industries, services, retail loans and other priority and non-priority sectors.

Renewable Energy in the Eurozone: Exploring Macroeconomic Impacts via FMOLS

This article examines the relationship between macroeconomic variables and the share of renewable energy in Eurozone countries from 2006 to 2020. Using the Fully Modified Ordinary Least Squares (FMOLS) method, we analyze the impact of Gross Domestic Product (GDP) per capita, unemployment rate, Financial Development Index (FDI), inflation, government efficiency, and corruption control on the proportion of renewable energy. Focused on the Eurozone, our study fills a gap in existing research. We compile diverse findings from the literature review on this topic. Our analysis reveals that higher GDP per capita positively influences the proportion of renewable energy, while unemployment, lower financial development, higher inflation, inefficient governance, and corruption negatively impact renewable energy adoption. These findings underscore the importance of addressing economic development alongside sustainable energy initiatives. Policymakers should prioritize improving GDP per capita, and addressing barriers such as unemployment and corruption to facilitate the transition to a more sustainable energy landscape in the Eurozone.

Asymmetric role of environmental policy stringency, fiscal, and monetary policy on environmental sustainability: Evidence from BRICS‐T countries

AbstractClimate change is often discussed and serves as an essential topic among policymakers and researchers. This study aims to examine the impact of macroeconomic policy and “environmental policy stringency” (EPS) on “ecological footprint” (EF) and “load capacity factor” (LCF) for the BRICS‐T (Brazil, Russia, India, China, South Africa, and Turkey) countries using annual data from 1990 to 2022. It also explores other environmental sustainability factors, such as economic growth and disintegrated energy. This investigation utilizes second‐generation approaches such as Westerlund cointegration, “method of moments quantile regression” (MMQR), and panel causality test. Furthermore, a robustness check is done using long‐run estimators, such as dynamic ordinary least square (DOLS), fully modified ordinary least square (FMOLS), and fixed effect ordinary least square (FE‐OLS). Overall, findings from the method of moments quantile regression (MMQR) test concerning EF suggest that economic growth and fiscal policy contribute to environmental quality. Meanwhile, monetary policy, renewable energy consumption, and environmental policy stringency mitigate the pollution level in all quantiles. Moreover, results from the LCF model indicate that monetary policy, environmental policy stringency, and renewable energy consumption improve the environmental condition. However, fiscal policy has damaged environmental pollution in each quantile. In addition, the study findings confirm the validity of the inverted U‐shaped Environmental Kuznets Curve (EKC) hypothesis and U‐shaped LCC hypothesis. The long‐run estimators support the outcomes from the MMQR method. The empirical findings of the DH panel heterogeneous causality test confirm the presence of bidirectional and unidirectional causality between the model parameters, underscoring that policy interventions such as government expenditure, money supply, and EPS significantly impact environmental quality and vice versa. Thus, the study recommends allocating more funds for eco‐friendly technology, initiating a green lending program, and enforcing stringent environmental laws and regulations to attain Sustainable Development Goals.

Oil rent, corruption and economic growth relationship in Nigeria: evidence from various estimation techniques

Purpose Despite the huge financial resources associated with oil, Nigeria has consistently recorded poor growth performance. Therefore, this study aims to examine how corruption and oil rent influence Nigeria’s economic performance during the 1996–2021 period. Design/methodology/approach Various estimation techniques were used. These include the bootstrap autoregressive distributed lag (ARDL) bounds-testing, dynamic ordinary least squares (DOLS), the fully modified OLS (FMOLS) and the canonical cointegration regression (CCR) estimators and the Toda–Yamamoto causality. Findings The bounds testing results provide evidence of a cointegrating relationship between the variables. In addition, the results of the ARDL, DOLS, CCR and FMOLS estimators demonstrate that oil rent and corruption have a significant positive impact on growth. Further, the results indicate that human capital and financial development enhance economic growth, whereas domestic investment and unemployment rates slow down long-term growth. Additionally, the causality test results illustrate the presence of a one-way causality from oil rent to economic growth and a bi-directional causal relationship between corruption and economic growth. Originality/value Existing studies focused on the effects of either oil rent or corruption on growth in Nigeria. Little attention has been paid to the exploration of how the rent from oil and the pervasiveness of corruption contribute to the performance of the Nigerian economy. Based on the outcome of this study, strategies and policies geared towards reducing oil dependence and the pervasiveness of corruption, enhancing human capital and financial development and reducing unemployment are recommended.

COINTEGRATING POLYNOMIAL REGRESSIONS: ROBUSTNESS OF FULLY MODIFIED OLS

Cointegrating polynomial regressions (CPRs) include deterministic variables, integrated variables, and their powers as explanatory variables. Based on a novel kernel-weighted limit result and a novel functional central limit theorem, this paper shows that the fully modified ordinary least squares (FM-OLS) estimator of Phillips and Hansen (1990, Review of Economic Studies 57, 99–125) is robust to being used in CPRs. Being used in CPRs refers to a widespread empirical practice that treats the integrated variables and their powers, incorrectly, as a vector of integrated variables and uses textbook FM-OLS. Robustness means that this “formal” FM-OLS practice leads to a zero mean Gaussian mixture limiting distribution that coincides with the limiting distribution of the Wagner and Hong (2016, Econometric Theory 32, 1289–1315) application of the FM estimation principle to the CPR case. The only restriction for this result to hold is that all integrated variables to power one are included as regressors. Even though simulation results indicate performance advantages of the Wagner and Hong (2016, Econometric Theory 32, 1289–1315) estimator, partly even in large samples, the results of the paper give an asymptotic foundation to “formal” FM-OLS and thus enlarge the usability of the Phillips and Hansen (1990, Review of Economic Studies 57, 99–125) estimator implemented in many software packages.

Nexus between profitability, firm size and leverage and tax avoidance: evidence from an emerging economy

PurposeThis study examines the impact of profitability, firm size and leverage on corporate tax avoidance in Bangladesh, an emerging South Asian economy.Design/methodology/approachA balanced panel data of 62 firms from Dhaka and Chittagong stock exchanges in Bangladesh from 2009 to 2020 were used to run the regression. This study employed the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) to examine the hypotheses.FindingsThe findings show that large firms positively impact corporate tax avoidance. Similarly, profitability and leverage are positively associated with tax avoidance, and the results are significant. Furthermore, the study conducts robustness tests that confirm the findings.Research limitations/implicationsThe use of cash effective tax rate (ETR) to investigate firms’ tax avoidance practices poses some limitations, and the results should be interpreted cautiously.Practical implicationsThe current study may help policymakers better enhance tax collection from business firms. The findings could serve as a valuable input for effectively monitoring tax collection from large profit-earning firms.Originality/valueTo the authors' best knowledge, this is the first historical attempt in Bangladesh to use panel data to examine the relationship between the firm’s level characteristics and corporate tax avoidance. Panel data often provides greater flexibility with large data, simplifying calculation and statistical analysis.

FDI-growth and trade-growth relationships during crises: evidence from Bangladesh

This study examines foreign direct investment (FDI)-growth and trade-growth relationships in Bangladesh during three major crises: the economic crisis of 2007–2008, the commodity crisis of 2016, and the coronavirus (COVID-19) pandemic of 2020. The augmented autoregressive distributed lag (AARDL) bounds testing approach and Bayer and Hanck cointegration are employed on time-series data spanning the period 1974–2020. The results suggest that exports have positive effects on economic growth, while imports have insignificant effects in both the short run and long run. Total trade (the sum of exports and imports) has a positive but weakly significant effect on economic growth only in the long run, whereas FDI exhibits a positive effect in both the short run and long run. Although the crises are not found to affect economic growth directly or through trade (i.e., no dampening effect on trade-led growth), they are found to distort FDI-led growth in both the short run and long run. As robustness tests for long-run elasticities, the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) cointegration techniques are implemented, yielding results similar to those obtained with the AARDL.

Dynamic nonlinear CO2 emission effects of urbanization routes in the eight most populous countries.

A dynamic STIRPAT model used in the current study is based on panel data from the eight most populous countries from 1975 to 2020, revealing the nonlinear effects of urbanization routes (percentage of total urbanization, percentage of small cities and percentage of large cities) on carbon dioxide (CO2) emissions. Using "Dynamic Display Unrelated Regression (DSUR)" and "Fully Modified Ordinary Least Squares (FMOLS)" regressions, the outcomes reflect that percentage of total urbanization and percentage of small cities have an incremental influence on carbon dioxide emissions. However, square percentage of small cities and square percentage of total urbanization have significant adverse effects on carbon dioxide (CO2) emissions. The positive relationship between the percentage of small cities, percentage of total urbanization and CO2 emissions and the negative relationship between the square percentage of small cities, square percentage of total urbanization and CO2 emissions legitimize the inverted U-shaped EKC hypothesis. The impact of the percentage of large cities on carbon dioxide emissions is significantly negative, while the impact of the square percentage of large cities on carbon dioxide emissions is significantly positive, validating a U-shaped EKC hypothesis. The incremental effect of percentage of small cities and percentage of total urbanization on long-term environmental degradation can provide support for ecological modernization theory. Energy intensity, Gross Domestic Product (GDP), industrial growth and transport infrastructure stimulate long-term CO2 emissions. Country-level findings from the AMG estimator support a U-shaped link between the percentage of small cities and CO2 emissions for each country in the entire panel except the United States. In addition, the Dumitrescu and Hulin causality tests yield a two-way causality between emission of carbon dioxide and squared percentage of total urbanization, between the percentage of the large cities and emission of carbon dioxide, and between energy intensity and emission of carbon dioxide. This study proposes renewable energy options and green city-friendly technologies to improve the environmental quality of urban areas.

Investigating the Effect of Trade Openness and Agriculture on Deforestation in Cameroon

This study aimed to measure the effect of trade openness and agriculture on deforestation in Cameroon from 1980 to 2021 by using a fully modified ordinary least squares (FMOLS) approach. Data used are from the World Bank and FAO. The results obtained indicate that when trade openness increases, deforestation also increases, but when trade openness increases up to a certain threshold, deforestation decreases. This study also reveals that agriculture is one of the major causes of deforestation in Cameroon. Agricultural output and agricultural value-added both have a positive and significant impact on deforestation. There is an inverted curve relationship between economic growth and deforestation in Cameroon, this shows that the EKC is respected with deforestation as it is postulated that at higher levels of income, GDP turns to reduce deforestation meaning a unit change in GDP2 leads to a reduction of deforestation. We recommend the implementation of concrete actions and strict environmental policies focused on a green economy, to control the exploitation of natural resources with particular attention to the sustainable exploitation of wood. Sustainable agricultural practices should also be implemented, as well as more suitable liberal trade policies.

Determinants of Life Insurance Consumption in OECD Countries Using FMOLS and DOLS Techniques

This paper aims to examine the determinants of life insurance consumption in 30 OECD countries using panel data from 1996 to 2020. This study uses GDP per capita, Life expectancy, Urbanization, School education, and Health expenditure as the determinants to measure the OECD countries’ life insurance consumption. Insurance density is used as a proxy for life insurance consumption. Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and causality tests are applied in this study. Our empirical results revealed that the variables urbanization, school education, and GDP per capita significantly impact life insurance consumption, whereas life expectancy and health expenditure were found to have an insignificant relationship in estimating life insurance consumption. These findings will help all insurance industry stakeholders in OECD countries in policy formulation and decision making.

Empowering tomorrow: unleashing the role of technology driven energy efficiency for Sustainable Development in China

The underlying objective of the study is to investigate the energy efficiency improvement potentiality of total technological innovation and its components, such as residential and non-residential technological innovations, by controlling for economic growth and energy prices in China. To this end, the study employs Dynamic Ordinary Least Squares (DOLS), Canonical Cointegrating Regression (CCR), and Fully Modified OLS (FMOLS) based on the unit root testing and Johansen and Juselius cointegration test outcomes. Thus, the results confirm the long-run relationship among the series under investigation. Further, it shows that total technological innovation and its components, i.e., residential and non-residential technological innovations, have the potential to improve energy efficiency. However, economic growth and energy prices are reducing energy efficiency. Moreover, these findings are stable while applying the other alternative methods for robustness. Therefore, the study suggests policy recommendations aimed at promoting technology that may be either residential or non-residential technological innovations to increase energy efficiency. Additionally, it urges designing national-level energy policy to boost economic progress by conserving energy along with measures to stabilize energy prices.

Taking flight: Exploring the relationship between air transport and Malaysian economic growth

All facets of the tourism business, especially air travel, have been affected by the COVID-19 pandemic. The aviation industry continues to contribute to international economic expansion significantly. The article aims to identify the impact of the development of air transport on the economic development of the Malaysian economy. Therefore, the air-transportation-led growth hypothesis (ALGH) was investigated by examining whether airline travel boosts the Malaysian economy. The effects of air passengers (a stand-in for tourism) on Malaysia's GDP were examined in the long and short term. This study analyzed Data for Malaysia using an autoregressive distributed lag (ARDL) technique. The time covered was from 1970 to 2020. Certain control variables (i.e., energy consumption, financial development, social globalization, and urbanization) were also analyzed to see how they influenced economic expansion. Statistically significant effects of air travel on GDP were found in both the short and long term of this study, suggesting that this mode of transportation contributes to the sustained expansion of Malaysia's economy. Several methods, including Canonical Cointegration Regression (CCR), Dynamic Ordinary Least Squares (DOLS), and Fully Modified Ordinary Least Squares (FMOLS), were used to verify the results independently. The article offers legislative suggestions for improving the aviation industry's productivity, security, and bottom line.

The role of the environmental subsystem in sustainable urban development: Evidence from megacities in China

Conflicts in urban subsystems have severely hindered the realization of sustainable development, among which the most serious is the conflict between the environmental subsystem and urban development. Differing from studies considering individual environmental elements, this paper innovatively investigates the quantitative relationship between overall environmental performance and other development dimensions to understand the quantitative role of the environmental subsystem in sustainable urban development. Taking the nine megacities in China as an example, this paper first develops the performance variables of four urban subsystems, including the environment, by entropy method and analyzes the conflict or coordination level between the environment and other subsystems through the coupling coordination degree model (CCDM). Then, the interaction mechanism is further analyzed by the fully modified ordinary least square (FMOLS) and vector error correction model (VECM). This paper tries to provide a new reference for management and decision-making by focusing on the whole environmental subsystem rather than separate elements, which is of theoretical and practical significance. The main conclusions are as follows: (1) The coordination level between the environment and other urban subsystems is low; (2) 1 % rise in the economic and resource performance can respectively lead to 0.2014 % and 0.1388 % declines in the environmental performance; (3) 1 % increase in social performance can bring a 0.3738 % rise in environmental performance; (4) Improving environmental and resource subsystems' performance is the priority; (5) Coordinating urban subsystems is the key to long-run sustainable development. Despite the case studies on megacities in China, we hope to provide a new reference for cities worldwide with concentrated populations, rapid growth, and complex development contradictions.

Green finance, social inclusion, and sustainable economic growth in OECD member countries

The importance of fostering a thriving green economy is emphasized as a key strategy for mitigating the threats posed by climate change. This study aims to assess the influence of social inclusion and green finance on the sustainable growth of 31 Organization for Economic Co-operation and Development (OECD) nations between 2010 and 2021. The results obtained using the fully modified OLS (FMOLS) methodology indicate that social factors such as poverty reduction and social inclusion do not impact the green economic prosperity of OECD countries with an industry-focused economic structure. Furthermore, the expansion of green finance markets and the attraction of foreign direct investment with an environmental focus can contribute to the advancement of green development in OECD countries. As a recommendation, it is advisable for OECD nations to place greater emphasis on developing the green digital finance market, exploring blockchain-based green finance solutions, fostering green foreign direct investment, and promoting early warning economic systems.

Exploring asymmetric influence of R&D expenditures on CO2 emissions in China: evidence from nonlinear ARDL model.

R&D spending upsurges technological advancement and innovation which results in lowering energy consumption and environmental degradation. The current study investigates the asymmetrical impact of R&D spending on CO2 emissions in China via employing annual data from 1980 to 2021 and the NARDL model for empirical analysis. The estimated results of the NARDL model confirmed that there are asymmetries in positive and negative coefficients of R&D spending in China. The results depict that the positive shock in R&D spending exerts a negative and statistically significant impact on CO2 emissions in both runs implying that an increase in R&D spending lowers CO2 emissions. However, the negative coefficient of R&D spending yields a positive and statistically significant impact on CO2 emissions revealing the fact that a negative shock in R&D spending results in the upsurge of CO2 emissions in China. According to these findings, the impact of positive and negative shocks in R&D spending on CO2 emissions is asymmetric. The findings also show that the impact of a negative shock in R&D spending is greater than the impact of a positive shock on CO2 emissions. In addition to the negative shock in R&D spending, increases in energy consumption, economic growth, and FDI inflows also contribute to an upsurge in CO2 emissions in China. The robustness of the estimated results is assessed using standard fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) models. The FMOLS and DOLS results have been confirmed to be sound and consistent with the results of the NARDL model. The study suggests that the economic strategies should aim at investing in R&D spending to foster environment-friendly technological innovations and to lower environmental degradation in China.

From brown to green: are Asian economies on the right path? Assessing the role of green innovations and geopolitical risk on environmental quality.

Preserving the sustainability of the natural environment has emerged as a critical focus on policy agendas worldwide. Therefore, this study examines the relationship between environmental quality and key determinants, focusing on geopolitical risk (GPR), green innovations (GI), economic growth, FDI, renewable energy consumption, and urbanization. Dataset is used for the time period of 1990-2020 across selected Asian economies including China, India, Japan, Malaysia, and South Korea. Using load capacity factor (LCF) as a comprehensive proxy for environmental quality, the research utilizes panel quantile regression (QR) to provide empirical outcomes. Results of panel QR method reveal a negative impact of economic growth and GPR on LCF. On the other hand, green innovation, FDI, and renewable energy are found as supportive factors to boost environmental quality. In addition, urbanization also shows positive linkage with LCF. The application of Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) further validates the robustness of the findings. Adoption of green innovations, practicing sustainable growth patterns, transition toward cleaner energy practices, and integrated urban planning are advocated to enhance environmental quality among Asian nations. Based on empirical findings study suggests comprehensive policy measures that can help in achieving sustainable development goals (SDGs) including SDG-7 (energy efficiency), SDG-8 (sustainable economic growth), SDG-11 (sustainable cities), and SDG-13 (climate action) among Asian countries.

Impact of Renewable and Non-Renewable Energy Resources on CO2 Emission: Empirical Evidence from SAARC

When assessing sustainability performance, researchers often ignore the implications of combining energy, economic, and environmental factors. To address this void, we assess the consequence of energy dis-aggregation proceeding carbon emissions in SAARC nations. The majority of energy resources are renewable and non-renewable, which contributes to a rise in carbon dioxide emissions. This study aims to provide a thorough understanding of the energy utilization dioxide emissions nexus. The panel data sets covering the period 1971-2020 for the SAARC nations have been used for analysis. The data set was used to assess the effect of renewable and non-renewable energy consumption on emissions of carbon dioxide by factoring in other CO2-producing variables such as urbanization, primary and secondary education, globalization, and economic development. Panel Fully Modified Ordinary Least Squares (FMOLS) was used to examine the connection. According to research comparing the impact of renewable and non-renewable energy consumption upon Carbon dioxide emission, whereas non-renewable energy consumption increases CO2 emissions, renewable energy consumption decreases them. Urbanization, globalization, primary education, and economic growth increase carbon emissions, while secondary education contributes to environmental quality improvement via CO2 reduction. Therefore, increasing the usage of renewable energy and enhancing awareness through higher education may help SAARC nations to reduce pollution emissions.

Achieving Carbon-Neutrality in MENA Countries: Does Financial Inclusion Matter?

This study examines the relationship between financial inclusion, renewable energy, and CO2 emissions using data from 11 Middle East and North Africa (MENA) countries from 2004 to 2019. Evidence from fixed effects-ordinary least squares (FE-OLS), dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and canonical correlation regression (CRR) showed that financial inclusion contributes significantly to decarbonization. Country-specific analysis indicated that financial inclusion is associated with mitigating CO2 emissions in Egypt, Israel, Qatar, and Tunisia while significantly spurring CO2 emissions in Algeria, Lebanon, and Saudi Arabia. In addition, renewable energy contributes significantly to decarbonization in MENA, especially in Algeria, Lebanon, Tunisia, and Turkey. We recommend that policies promoting financial inclusion and renewable energy usage would contribute to the attainment of the carbon-neutrality goal by MENA countries.

International Trade and Women Employment: The Case of Jordan

Objectives: This study aims to assess the impact of international trade measurements, trade liberalization, and other related variables on the Female Labor Force Participation (FLFP) and Female Employment (FE) in the agricultural, industrial, and services sectors in Jordan from 1990 to 2019. Methods: The study adopts four models and uses the Fully Modified Ordinary Least Square (FMOLS) approach to estimate the effects of International Trade (Trade), Gross Domestic Product per Capita (GDPC), Live Births (LB), Female Enrollment in Secondary School (EFSS), Female Unemployment (FUN), and Jordan’s Accession to the WTO on the FLFP and FE in the three sectors. Results: The empirical results indicate that international trade measurements, Jordan’s WTO accession, EFSS, and FUN have a significantly negative impact on FLFP, except for GDPC which reveals a positive impact on FLFP, while LB proves to be insignificant. Different results were obtained in terms of Female Employment (FE) in the three economic sectors. The trade measurements hurt FE in the agricultural and services sectors, and a positive impact on the industrial sector. Conclusions: One of the most intriguing findings of this study is that Female Employment (FE) in Jordan has experienced adverse impacts due to increased trade openness. Consequently, the government must pursue policies promoting trade openness without adversely affecting FE. This can be achieved by enhancing Female Labor Force Participation (FLFP) through education and training initiatives. Simultaneously, there is a need to consider revising labor laws and regulations. These revisions should unequivocally prohibit gender-based wage discrimination.

Exploring the impact of economic growth and energy consumption on SO2 emissions in China based on the Environmental Kuznets Curve hypothesis

This study aims to analysis the influence of economic growth (EG) and energy consumption (EC) on sulfur dioxide emissions (SE) in China. Accordingly, this study explores the link between EG, EC, and SE for 30 provinces in China over the span of 2000–2019. This study also analyzes cross-sectional dependence tests, panel unit root tests, Westerlund panel cointegration tests, Dumitrescu-Hurlin (D-H) causality tests. According to the test results, there is an inverted U-shaped association between EG and SE, and the assumption of the Environmental Kuznets Curve (EKC) is verified. The signs of EG and EC in the fixed effect (FE) and random effect (RE) methods are in line with those in the dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS) and autoregressive distributed lag (ARDL) estimators. Moreover, the results verified that EC can obviously positive impact the SE. To reduce SE in China, government and policymakers can improve air quality by developing cleaner energy sources and improving energy efficiency. This requires the comprehensive use of policies, regulations, economic incentives, and public participation to promote sustainable development.