Search

Abstract Understanding the risks posed to humankind, the environment, and overall growth requires a deep exploration of the profound impact of greenhouse gas (GHG) emissions, especially carbon dioxide (CO2), on global climate change. This study explores the complex relationships among economic extension, energy utilization, financial progress, natural resource rents, and CO2 emissions in Bangladesh. By analyzing annual data from 1974 to 2022, our analysis reveals a long-term connection between the variables, as evidenced by the autoregressive distributed lag (ARDL) bounds test. We found that for every 1% rise in economic growth, energy use, financial progress, and natural resource rents, CO2 emissions rise by 0.13%, 1.50%, 0.39%, and 0.04%, respectively. This is based on the dynamic ordinary least squares (DOLS) method. The outcomes are further supported by the canonical cointegrating regression (CCR), followed by fully modified ordinary least squares (FMOLS). By illuminating the causes of Bangladesh's environmental degradation, this study offers actionable policy suggestions aimed at fostering sustainable growth and enforcing strict regulations that effectively mitigate environmental harm. Graphical Abstract

Abstract Carbon dioxide (CO2), whose atmospheric volume has been increasing, is the principal greenhouse gas (GHG) that causes global warming and climate change. Climate change from increasing greenhouse gases (GHGs) has broad health and environmental influences. Human-emitted GHGs constitute a significant cause of global warming. The main objective of this article is to assess the influence of technological innovation, economic growth, fossil energy use, renewable energy consumption, and globalisation on CO2 emissions (CO2Es) in South Africa over the observation period 1990–2020. The Dynamic Ordinary Least Squares (DOLS) estimator was implemented to examine the relationship between the constituents. The study results indicate that economic growth, fossil energy use, technological innovation, and globalisation boost CO2Es, whereas renewable energy consumption reduces CO2Es. The output of the canonical cointegrating regression and fully modified least squares estimators also supports the output of the DOLS estimator. We concluded that, in South Africa, a rise in economic growth, fossil energy use, technological innovation, and globalisation results in an upsurge in CO2Es. In contrast, an increase in renewable energy consumption leads to a reduction in CO2Es. This research work contributes to the discourse on CO2Es in the South African context. The study recommends that policymakers in South Africa should design policies that foster the formation of a low-carbon country, the consumption of renewable energy, and the implementation of environment-friendly technological innovations that reduce the use of fossil energy. Graphical Abstract

Global climate change triggered by the escalating carbon dioxide (CO2) emissions resulting from heightened economic expansion and consumption of energy represents the foremost environmental concern in the contemporary global context. China is accountable for nearly 27% of total global CO2 emissions, making it the largest emitter worldwide. This study explores the consequences of economic outgrowth, fossil fuel, and renewable energy utilization on China's emissions of CO2. The study utilized an autoregressive distributed lag (ARDL) model to analyze China's annual data from 1965 to 2022. The ARDL bounds test results indicate the presence of long-term cointegration among the variables. The empirical results specify that a 1% growth in the utilization of fossil fuels and the economy leads to long-term growth of 4.97% and 0.58% in CO2 emissions. In the short term, these increases amount to 2.42% and 1.21%, respectively. Conversely, a long-term enhancement of 1% in renewable energy utilization could result in a reduction of CO2 emissions by 1.39%, whereas in the short term, the reduction would be 0.50%. This study proposes policy suggestions for the attainment of sustainable development and carbon neutrality.

Abstract Tourism serves as a crucial means of funding national development and ensuring the sustainability of local livelihoods in growing countries such as Brazil. Nevertheless, the accelerated growth of tourism in various nations might lead to significant environmental consequences due to heightened energy consumption. This surge in energy usage contributes to the exacerbation of global warming through the amplified release of carbon dioxide (CO2). In light of the increasingly evident impacts of climate change on the natural environment, a multitude of endeavors have been undertaken with the aim of attaining ecological sustainability. The objective of this study is to examine the dynamic effects of economic growth, energy consumption, and tourism on carbon emissions in Brazil by analyzing time series data spanning from 1990 to 2019. The stationarity of data was assessed through the application of unit root tests, while an autoregressive distributed lag (ARDL) approach was employed to analyze the relationship between the components, accounting for both the long-term and short-term dynamics. The empirical results indicate that a 1% rise in economic growth, energy consumption, and tourist arrivals leads to environmental deterioration by causing a respective increase in CO2 emissions of 1.37%, 1.06%, and 0.57% in the near term, and 0.72%, 0.62%, and 0.16% in the long term. This article presents policy ideas aimed at achieving carbon neutrality in Brazil by increasing the utilization of renewable energy sources, while simultaneously fostering sustainable tourism as a means to enhance the country’s economy. Highlights •This study investigated the tourism-economy-energy-environment nexus in Brazil. •The econometric analysis revealed that tourism, energy use, and economic growth increase CO2 emissions. •This article offers policy suggestions to achieve sustainable tourism and carbon neutrality. Graphical Abstract

This study investigates the relationship between the concepts of digital economy and sustainability. The increasing popularity of these concepts can be attributed to the growing recognition of climate change and the subsequent advancements and acceptance of technological innovations. Researchers, professionals, and policymakers are currently exploring the various uses of digital technologies to address sustainability issues. This study conducted a systematic literature review to identify research efforts related to the opportunities and challenges of the digital economy concerning sustainability. The study has identified prospects for the interconnection of renewable energies and sustainable technological solutions, the use of big data analytics to promote sustainability, consumer empowerment and value co-creation, sustainable urbanization, circular economy principles implementation, sustainable consumption promotion, and the adoption of socially responsible business models. Companies can adopt sustainable business strategies and develop sustainable products by utilizing digital technologies. However, the digital economy faces obstacles that could hinder the achievement of sustainability goals. The challenges encompass the increase in electronic waste, higher energy consumption and carbon emissions, the presence of a digital divide, job insecurity, the growth of monopolies, and concerns about data protection. To advance sustainability through the digital economy, it is crucial to address these challenges.

The main purpose of this paper is to investigate the environmental repercussions of a booming economy, fossil fuel, renewable energy, globalisation, technological innovation, and forest cover in Poland from 1990 to 2018. This study used greenhouse gas (GHG) emissions and load capacity factor as proxies of ecological damage to provide a comprehensive assessment of the surrounding ecosystem in Poland to help implement future policies to reduce emissions and achieve environmental sustainability. Besides, Poland is the only European Union (EU) country lacking a climate neutrality objective and policy instruments to mitigate ecological concerns. The Autoregressive Distributed Lag (ARDL) bounds model and the Dynamic Ordinary Least Squares (DOLS) procedure evaluated yearly data from 1990 to 2018. The empirical outcomes of the DOLS point out that economic growth and fossil fuel energy use enhance GHG emissions despite reducing the load capacity factor in Poland. Instead, renewable power, globalisation, technological innovation, and forest cover might help to reduce GHG emissions and enhance the load capacity factor in Poland. The concluding thoughts from this study suggest the implementation of policies that support raising additional funding for eco-friendly technology studies while promoting renewable energy use and enhancing forest carbon sinks.

Global warming, induced by human-generated greenhouse gases, especially carbon dioxide (CO2), threatens the planet's ecology, economic development, and long-term viability in unparalleled ways. Vietnam's utilization of energy and CO2 emanations are on the increase as a consequence of the country's increasing economic and improving agricultural practices. Governments seeking to accomplish an equilibrium between combating climate change and sustainable development would benefit from a deeper understanding of Vietnam's climate change vulnerability. A better understanding of the trade-off between pollution and economic growth is crucial if Vietnam is to slow the rate at which its environment is being damaged. So, the current study empirically examined the connections between GDP development, energy consumption, technical advancement, agricultural output, forest region, and carbon dioxide emissions in Vietnam. This study collected annual data from 1990 to 2020 and analyzed it using the Dynamic Ordinary Least Squares method. Based on the projection, Vietnam experiences a direct correlation between energy consumption and CO2 emissions. This implies that an increase in energy consumption and economic growth corresponds to a corresponding emission of carbon dioxide into the atmosphere. Nevertheless, it is essential to acknowledge that this pattern might be alleviated by means of technological innovations, increased agricultural output, and initiatives to broaden forested regions. A negative correlation exists between these variables and CO2 emissions; technological innovations, more sustainable agricultural methods, and forest preservation initiatives may contribute to the gradual reduction of CO2 emissions. Therefore, environmental sustainability could be achieved through reduced emissions if the policies advocated in this article were implemented. Some of these policies include using renewable energy sources, encouraging innovations in technology, encouraging climate-conscious agriculture, and encouraging careful forest management.

The significance of natural resources in facilitating financial activity is crucial in achieving a nation's sustainable economic advancement. This research directed to investigate the consequences of economic growth, rents of natural resources, and capitalization on financial development within the context of the United States (US). The investigation utilized the autoregressive distributed lag (ARDL) simulation, employing a dataset spanning the years 1970–2021. The outcome of the ARDL bound test supported the occurrence of cointegration midst the factors. The conclusions of the empirical analysis suggest that an outgrowth of 1% in natural resource rent, economic expansion, and capitalization would lead to a corresponding long-term increase of 0.30%, 0.26%, and 0.25% in financial development. Additionally, in the near term, these factors would contribute to an expansion of 0.22%, 0.22%, and 0.19% in financial development. Besides, the probe employed the Granger causality check to inspect the causal liaison concerning the factors. The investigation's results would stipulate visions for legislators in formulating a complete policy intended at augmenting the connection between finance and economy, with a specific focus on operating natural resources as a means to achieve sustainable development.

The apparel manufacturing industry (AMI) significantly contributes to the economic growth of various developing nations. However, unsustainable production practices remain a major concern in this sector. Therefore, this study investigated the most significant challenges to adopting sustainable production practices in the apparel manufacturing sector from the perspective of an emerging economy like Bangladesh. Initially, the study identified the significant challenges through an extensive literature review and expert validation. Later, by utilizing a Grey Decision-Making Trial and Evaluation Laboratory (Grey DEMATEL) framework, the challenges were analyzed, and the interrelations among them were explored. The findings revealed that the ‘slow return on investment’, ‘lack of proper waste management systems’, and ‘reluctance to adopt advanced manufacturing processes’ are the top most significant challenges to adopting sustainable production practices in the apparel manufacturing sector. A sensitivity assessment was also performed to appraise the robustness of the obtained result. The outcomes of this research are expected to assist managers and practitioners in the AMI and other relevant industrial sectors in strategic planning to achieve various sustainable development goals (SDGs).

This study aims to investigate the long-term relationship between Malaysian tourism, energy consumption, and their association with economic growth, and financial development, using the Autoregressive Distributed Lag (ARDL) method. Utilizing annual data spanning from 1990 to 2020, this research examines how increased international tourist arrivals, economic growth, and financial development contribute to energy consumption in Malaysia. The study's findings reveal that growing tourism escalates energy demands for transportation, accommodation, and leisure activities. Furthermore, economic expansion drives energy usage through business expansion and increased industrial activities. Financial progress facilitates capital accessibility, leading to investments in energy-intensive sectors. In conclusion, to ensure sustainable future energy demand, this study underscores the importance of eco-friendly tourism practices that stimulate economic growth while minimizing emissions. Sustainable economic and financial strategies are also essential.

The insidious economic impact of water disruption has received less attention compared to palpable climate disasters, but climate change and water security concerns call for investigation on cost consequences of property values and business losses in an already fragile water supply-demand balance in many developing countries. The economic impact of frequent water supply disruptions from the Selangor River in Malaysia due to technical issues, water pollution, and climate-related problems was estimated using the stock flow measure to assess property at risk, and survey to estimate business loss. The findings revealed that commercial, residential, and industrial property valued at RM459,041 million in 2020 in the State of Selangor and parts of Kuala Lumpur were at risk due to frequent water supply disruptions. A survey of small and medium enterprises from the manufacturing, construction, and services sectors revealed that 46% of the respondents were affected with losses amounting to RM2,053 million. The total economic impact of water supply disruption in 2020, combining both property value at risk and business loss, amounted to RM461,094 million, which accounted for 34% of Malaysia's GDP. A number of recommendations are made to prevent the widespread occurrence of water disruptions. HIGHLIGHTS Water supply disruption causes huge economic impacts in Malaysia.; The main causes of water supply disruptions are technical problems, pollution, and climatic hazards.; This study estimated total property value at risk and business loss due to water supply disruption.; Climate change puts pressure on water resources in Malaysia.; The findings offer long-term decision-making solutions for water resource management.;

The energy demand in Australia is increasing with the industrialization and rapid economic growth. This study analyzed the relationships among the economic growth, Foreign Direct Investment (FDI), trade openness, urbanization, and energy usage in Australia based on the data from World Development Indicators (WDI) from 1972 to 2021. The results indicated that there is a cointegration among economic growth, FDI, trade openness, urbanization, and energy usage, which was traced through the autoregressive-distributed lag (ARDL). The Zivot-Andrews unit root test reveals that energy usage, economic growth, FDI, urbanization, and trade openness show significant structural breaks in 1993, 1996, 1982, 2008, and 1994, respectively. The ARDL model shows that economic growth has a positive and significant effect on energy usage in the long-run (0.814) and short-run (0.809). Moreover, the results also show that FDI (0.028) and trade openness (0.043) have positive impacts on energy usage in the long-run. However, urbanization shows a negative and significant influence on energy usage in the long-run (−0.965). Then, the research demonstrates a unidirectional causation between energy usage and trade openness, with energy usage significantly causing trade openness. The current study endorses energy consumption policies and investment strategies for a paradigm shifting from a reliance on fossil fuels as the primary energy source to renewable energy sources. These findings have profound implications for sustainable energy usage.

This study analyzes the impact of economic expansion, fossil fuel consumption, renewable energy adoption, and health expenditure on carbon dioxide (CO2) emissions in Malaysia from 1990 to 2021. The autoregressive distributed lag (ARDL) method is used to examine short and long-term consequences. The ARDL bounds test revealed evidence of cointegration among the variables in the long run which is verified by the Johansen cointegration test and Engle-Granger cointegration test. The empirical results suggest that increased economic expansion and fossil fuel usage are associated with higher CO2 emissions. In contrast, renewable energy use and health expenditure are linked with lower CO2 emissions in the long term. The Toda-Yamamoto causality test is also employed to investigate causal relationships between variables. The study recommends that the Malaysian government invest in green technology and promote policies that encourage sustainable practices. A green growth approach can ensure economic growth, mitigate environmental challenges, and achieve sustainable development goals. Health initiatives should also be prioritized to promote economic growth while mitigating environmental damage. This approach can reduce the country's reliance on fossil fuels, create new employment opportunities, and contribute to the overall well-being of the population. In conclusion, Malaysia's economic growth has led to an increase in energy demand and CO2 emissions. However, adopting a green growth approach by investing in renewable energy, sustainable practices, and health initiatives can pave the way for a sustainable and prosperous future.

Abstract Agriculture is one of the major sources of global emissions that cause climate change while agricultural value added helps to boost the economy in developing countries like China. Therefore, this study aims to investigate the long- and short-term influences of agricultural value added, economic growth (GDP), and energy use on carbon dioxide (CO2) emissions in China. The autoregressive distributed lag (ARDL) method was used by using annual time series data from 1990 to 2021. The empirical outcomes revealed that a 1% increase in the agricultural value added would cut CO2 emissions by 1.37% in the long-run and 0.65% in the short-run. However, this study found that both GDP and energy consumption have a positive and statistically significant effect on CO2 emissions. Furthermore, an inverted U-shaped association between economic growth and environmental pollution was discovered by spotting the positive coefficient of GDP and the negative coefficient of GDP squared, which proved the validity of the environmental Kuznets curve (EKC) hypothesis. The robustness of the ARDL outcomes was verified by using the fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegration regression (CCR) approaches. This study offers a comprehensive set of policy recommendations aimed at enhancing agricultural value added in China. These suggestions focus on the promotion of climate-smart agriculture, the integration of renewable energy sources in agricultural production, and the adoption of advanced technologies within agricultural systems. Implementing these measures would contribute to the achievement of China’s goal of carbon neutrality. Graphical Abstract

Adverse consequences are observed in developing countries due to the impact of the globalization process. Therefore, our study aims to empirically verify whether globalization escalates carbon dioxide emissions in selected N-11 (next-11) countries between 1990 and 2019. The study also analyzes how per capita GDP, per capita GDP2, population growth, and renewable energy consumption affect carbon emissions. For this reason, the researchers used several econometric methods, including the slope homogeneity test, the cross-sectional dependency test, the panel unit root test, the panel cointegration test, the method of moment’s panel quantile regression analysis, and the Wald test. The estimated results of panel quantile regression show how carbon emissions change across a range of quantiles (0.1 to 0.9). The findings show that per capita GDP significantly impacts the overabundance of carbon emissions in N-11 countries. Over time, the study found that the positive coefficient value of per capita GDP decreased from the first to the last (7.41 to 5.87), leading to the validation of the EKC hypothesis. The adverse correlation between per capita GDP2 and environmental contamination confirms that the Environmental Kuznets Curve hypothesis is valid for selected N-11 countries. Globalization deteriorates the environment by directly affecting CO2 emissions. It increases monotonically from the lower quantile to the upper quantile (0.972 to 1.002). At the quantile level of 0.1 to 0.9, population growth and renewable energy consumption increase impede carbon dioxide emissions in these selected countries. Coefficient values in the quantile 0.1 to 0.9 (-0.35 to -0.53) suggest that governments can reduce carbon emissions more due to renewable energy consumption over time. But the negative coefficient values of the population (-0.97, -0.93, -0.90, -0.88, -0.86, -0.85, -0.83, -0.81, and -0.77) decrease from the lower quantile to the upper quantile. The Wald test supports the asymmetric effects of different quantiles. As a robustness check of estimators, the study used FMOLS, DOLS, and CCR, which show the variables’ long-run elasticity. The research developed targeted policy recommendations for sustainably mitigating carbon emissions based on the above results.

The escalating demand for fossil fuels in India has resulted in a significant strain on the environment due to the amplified emissions of greenhouse gases (GHGs). Nevertheless, India possesses a significant capacity for nuclear energy, as seen by its 22 operational nuclear reactors. This capacity holds promise for reducing GHG emissions. This research utilized a dataset spanning from 1969 to 2021 to examine the impact of nuclear energy utilization on GHG emissions. The study also aimed to verify the validity of the environmental Kuznets curve (EKC) hypothesis in India, employing the Autoregressive Distributive Lag (ARDL) model. The findings suggest that the EKC is applicable to India. Furthermore, it has been observed that a marginal increase of 1% in the use of nuclear energy leads to a reduction of GHG by 0.02% in the immediate term and 0.16% in the long term. This finding highlights the significance of expanding nuclear power as a crucial policy objective in order to attain environmentally friendly and sustainable economic development.

Mangroves are imperative to coastal systems, providing security against extreme weather events by acting as natural barriers. These halophytic plants play a crucial role in mitigating global warming and act as an invaluable resource for consumption. Despite proving to be resilient, mangroves exhibit sensitivity to climatic (e.g., Land Surface Temperature, Salinity, etc.) and man-made factors (e.g., Land Use/Land Cover Changes). Numerous past studies recording the relationship between mangrove growth & development with the aforesaid constituents, but those were mostly restricted to visual observation/pattern recognition and single type of regression analysis. Also, the evaluation of simultaneous exploration of multiple aspects influencing mangrove evolution was not prominent. Therefore, the main objective of this study was to focus on the impact of both salinity and land surface temperature on mangrove biomass by the joint-venture of remote sensing, geographic information system and several machine learning algorithms. The study considered appropriate mangrove site selections with pre-processing of the acquired satellite images. Also, mathematical computations were performed on the raster layers to determine the previously mentioned natural aspects. Finally, several types of regression analysis were conducted to delineate potential patterns, governing mangrove vegetation health by virtue of temperature and salinity. Mangroves’ relationship with temperature and salinity showed insignificant coefficient of determination. However, the generated response curves postulated that high mangrove biomass could be achieved for a specific temperature window (30-33◦C) and vegetation health could deteriorate at increasing salinity. Overall, combined effects of surface temperature and salinity on mangrove vegetation were significantly more (i.e., Maximum coefficient of determination of 0.31) than individual component alone.

ABSTRACTThe child mortality rate for children under five years of age is a major problem worldwide. The purpose of this study is to investigate the factors responsible for controlling the under-5 mortality rate in G-7 countries. The study utilized monthly time series data from 1971 to 2021 and employed Grossman’s health production function with Driscoll-Kraay’s standard error estimation approach for all statistical analyses. The results showed that economic expansion and the use of renewable energy contribute to the reduction of under-5 mortality rates in G-7 countries, whereas fossil fuels, industrialization, education, and unemployment contribute to the increase in mortality rates. The robust results also revealed that GDP, renewable energy, and education help mitigate under-5 mortality rates in G-7 countries. This analysis plays an essential role in helping G-7 countries reduce their under-5 mortality rates while considering environmental sustainability. Additionally, the required discussions and policy recommendations are also presented.

The utilization of fossil fuels and deforestation are the primary causes of global warming and climate change. Innovation is therefore essential to achieve green energy and sustainable development. As part of the Paris Agreement, Colombia has pledged to cut 51% of its emissions by 2030 and achieve net zero emissions by 2050. Thus, the present study examined the emission reduction potential of economic growth, renewable energy utilization, globalization, technological innovations, and forests in Colombia by employing the Dynamic Ordinary Least Squares (DOLS) technique utilizing time series data from 1990 to 2020. The empirical results revealed that a rise in economic growth is connected with a boost in emissions, while a boost in the consumption of renewable energy, technical innovation, globalization, and forest cover might cut Colombia's carbon emissions in the long run. Furthermore, a credibility test was conducted by utilizing total greenhouse gas emissions as other measurements of emission reduction. The DOLS results are stable while applying the fully modified least squares (FMOLS) and canonical cointegrating regression (CCR) techniques. This article includes policy ideas aiming at a low-carbon economy, promotion of renewable energy, financing technical innovation, globalization, and sustainable forest management to meet Colombia's goal of emission reduction.

Global warming and climate change are caused by anthropogenic greenhouse gas (GHG) emissions from rising energy consumption due to population and economic growth. Over the past decade, information and communication technology (ICT) usage has increased which may increase energy utilization and GHG emissions. Conversely, ICT's clean technologies can minimize pollution. Therefore, this study investigated the dynamic influences of ICT, economic growth, population, and energy use on GHG emissions in Malaysia by employing the autoregressive distributed lag (ARDL) approach using data from 1990 to 2021. The results of the ARDL bounds test indicated the existence of a long-run link between the variables. The results revealed that a 1% increase in the number of Internet users could increase GHG emissions by 0.013% in the short run but cut GHG emissions by 0.077% in the long run. Additionally, economic growth, population, and energy usage have a positive association with GHG emissions. The robustness of the ARDL outcomes has been checked by using the Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegration Regression (CCR) methods. This article offers additional policy proposals for emission reduction and sustainable development by promoting renewable energy use and financing green ICT.

Unquestionably, the industrial revolution of the twenty-first century contributes to global warming. Excessive amounts of carbon emissions into the atmosphere are responsible for global warming. Therefore, this research aims to assess the impact of GDP, green energy consumption, population, trade openness, and democracy on CO2 emissions in four selected South Asian countries from 1990 to 2019. This research also attempts to evaluate the EKC hypothesis in terms of economic growth (GDP2). The unit root of panel data and cointegration tests are executed in this study as a prelude to the regression analysis. Quantile regression for panel data, which (Powell, 2016) devised to deal with the fixed effect problem, is used in this study, and (Powell, 2016) empirical findings are the main focus. The estimated coefficient of GDP is positively significant, demonstrating that economic activity increases the burning of fossil fuels and upsurges atmospheric CO2 emissions. After attaining economic development, the reversed U-shaped EKC theory is valid for four selected South Asian countries. Economic development encourages these countries to use green technology, which helps mitigate CO2 emissions. The research, however, reveals that green energy is to blame for CO2 emissions. Burning biomass releases carbon dioxide that negatively impacts the quality of the environment. The study confirms that human activities are the leading contributor to environmental deterioration. Population growth has a worsening effect on the environment. The association between population and CO2 emissions is positively significant. The estimated coefficient of trade openness is positive, which increases CO2 emissions significantly. The estimated coefficient of democracy is quite negative. Therefore, this study suggests prioritizing democracy to reduce CO2 emissions. Citizens who live in democracies are better informed, more organized, and able to protest, all of which contribute to increased government responsiveness to environmental preservation. The results of the Wald test support the differential effects at various quantiles. The Dumitrescu-Hurlin (2012) panel causality tests are also used in this analysis to check causality between variables. Based on the findings, this research makes many policy suggestions for lowering carbon emissions.

The environmental challenges that have arisen as a result of rapid economic growth have become a hindrance to social progress. This article examines the effects of energy consumption, urbanization, industrialization, economic development, and technological innovations on South Korea's greenhouse gas (GHG) emissions. Using data from 1990 to 2021 and the autoregressive distributed lag (ARDL) method for empirical investigations, the findings indicate that fossil fuel energy consumption, urbanization, industrialization, and economic expansion all pose a threat to environmental sustainability due to their positive impact on GHG emissions. In contrast, the results suggest that renewable energy usage and technological innovations improve environmental sustainability by reducing GHG emissions in both the short and long term. In addition, the findings were validated using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegration Regression (CCR) techniques. The most significant contribution is that the findings of this study provide various policy recommendations for achieving environmental sustainability and net zero emissions in South Korea.

This study investigated the dynamic effects of economic growth, renewable energy consumption, urbanization, industrialization, tourism, agricultural productivity, and forest area on carbon dioxide (CO2) emissions in Chile. Using the dynamic ordinary least squares (DOLS) approach, time series data from 1990 to 2020 were analyzed. A 1% increase in economic growth, urbanization, industrialization, and tourism will increase Chile's CO2 emissions by 0.62%, 0.24%, 0.15%, and 0.1%, respectively. Additionally, a 1% increase in renewable energy consumption, agricultural production, and forest area may lead to CO2 emission reductions of 0.55%, 0.20%, and 0.98%, respectively. The estimated outcomes are insensitive to alternative estimators such as fully modified ordinary least squares (FMOLS) and canonical cointegrating regression (CCR). In order to ensure environmental sustainability through emission reductions, this article recommends policies on low-carbon economies, renewable energy consumption, sustainable urbanization, green industrialization, eco-friendly tourism, climate-smart agriculture, and sustainable forest management.

Global climate change, exacerbated by greenhouse gas (GHG) emissions, notably carbon dioxide (CO2), provides a huge danger to lives, the global environment, and development. The current study explored the dynamic effects of economic growth, fossil fuel energy consumption, renewable energy consumption, tourism, and agricultural productivity on CO2 emissions in Egypt. The Dynamic Ordinary Least Squares (DOLS) method was used to analyze time series data from 1990 to 2019. The empirical findings revealed that, while economic growth, the use of fossil fuel energy, and tourism contribute to environmental damage by cumulative CO2 emissions in Egypt, an increased share of renewable energy and agricultural productivity contribute to improved environmental quality by lowering CO2 emissions. Similar results were obtained using alternative estimators such as fully modified least squares (FMOLS) and canonical cointegrating regression (CCR). Furthermore, the pairwise Granger causality test was used to determine the causal relationship between the variables. This study adds to the current literature by putting light on the causes of pollution in Egypt. This article made policy ideas for a low-carbon economy, boosting renewable energy consumption, green tourism, and climate-smart agriculture, all of which would assure environmental sustainability in Egypt by lowering emissions.

Emissions of greenhouse gases (GHG), and carbon dioxide (CO2) represent a significant threat to environment, economy, and human health. This article summarises the results of an empirical investigation on the potential for economic growth, renewable energy use, technical advancement, and forest cover in Indonesia to reduce CO2 emissions. This study utilized the Dynamic Ordinary Least Squares (DOLS) approach to analyze the time series data covering 1990–2020. The empirical statistics suggest that Indonesia's CO2 emissions would increase by 1.17% for every 1% increase in economic development. Furthermore, a 1% increase in the use of renewable energy may be connected with a 1.40% decrease in CO2 emissions, technical innovation with a 0.17% decrease, and an increase in forest cover with a 3.94% decrease in CO2 emissions. The estimated outcomes are insensitive to alternative estimators such as fully modified ordinary least squares (FMOLS) and canonical cointegrating regression (CCR). Furthermore, the pairwise Granger causality test was employed to capture the causal relationship between the variables. This article presents policy suggestions for achieving environmental sustainability in Indonesia through the reduction of carbon emissions. These policies emphasize a low-carbon economy, promoting renewable energy use, financing technical advancement, and the ecological viability of Indonesia's forests.

Global climate change triggered by greenhouse gases (GHGs) puts incomparable threats to the environment and food security. Agriculture is one of the key drivers of environmental deterioration, which is linked to GHG emissions and labeled ultrasensitive to climate change. However, there is a scarcity of research exploring the nexus between agriculture and GHG emissions in Bangladesh. Thus, the present study empirically investigates the dynamic impacts of agricultural land expansion, agricultural value added, crop production, livestock production, fisheries production, energy use in agriculture, fertilizer consumption, and forest land on GHG emissions in Bangladesh. Time series data from 1990 to 2018 were utilized by employing the Dynamic Ordinary Least Squares (DOLS) approach. The empirical findings reveal that a 1% increase in agricultural land, crop production index, livestock production index, fisheries production, energy use in agriculture, and fertilizer consumption will increase GHG emissions by 0.25%, 0.29%, 0.40%, 0.18%, 0.46%, and 0.28% in the long run. Conversely, a 1% increase in agricultural value added and the forest land may lead to GHG emissions reduction by 0.32% and 1.44% in the long run. The estimated results are robust to alternative estimators such as fully modified least squares (FMOLS) and canonical cointegrating regression (CCR). This research contributes to the existing literature by shedding light on the GHG emissions from the agriculture sector of Bangladesh. This article put forward policy recommendations on sustainable and climate-smart agriculture that would enhance productivity and resilience while reducing emissions from the agriculture sector.

Global climate change, intensified by greenhouse gas (GHG) emissions, particularly carbon dioxide (CO2), provides a massive threat to human life, the environment, development, and sustainability. Consequently, reducing emissions and improving environmental conditions are now a global priority for promoting sustainable growth and preventing the adverse consequences of global warming and climate change. However, there is a paucity of research employing econometric methodologies to investigate the potential of emission reduction factors, particularly in the case of the Philippines. Thus, the present study explored the dynamic effects of economic growth, renewable energy use, urbanization, industrialization, tourism, agricultural productivity, and forest area in the Philippines in order to accomplish environmental sustainability by lowering CO2 emissions. The Autoregressive Distributed Lag (ARDL) bounds testing approach and the Dynamic Ordinary Least Squares (DOLS) methodology were used to scrutinize time series yearly data from 1990 to 2020. According to the estimates, a 1% rise in economic growth, urbanization, industrialization, and tourism will raise CO2 emissions by 0.16%, 1.25%, 0.06%, and 0.02% in the Philippines, respectively. Furthermore, a 1% increase in renewable energy consumption, agricultural productivity, and forest area may result in CO2 emissions reductions of 1.50%, 0.20%, and 3.46%, respectively. Furthermore, the pairwise Granger causality test was employed to capture the causal relationships between the variables. This article provides policy implications on low-carbon economies, promoting renewable energy use, sustainable urbanization, green industrialization, eco-friendly tourism, climate-smart agriculture, and sustainable forest management, all of which could help ensure environmental sustainability by lowering emissions.

Global climate change, aggravated by greenhouse gas (GHG) emissions, particularly emissions from carbon dioxide (CO2), poses significant threats to the environment, development, and sustainability. Mexico is a prominent tourist country experiencing continuous economic growth, rapid urbanization, and agricultural productivity which is causing higher energy consumption and CO2 emissions. The current research explores the dynamic effects of economic growth, fossil fuel energy use, renewable energy use, urbanization, tourism, and agricultural productivity on CO2 emissions in Mexico. Time series data from 1990 to 2019 were analyzed using the Dynamic Ordinary Least Squares (DOLS) method. According to the estimations, a 1% rise in economic growth, fossil fuel energy use, urbanization, and tourism will raise CO2 emissions by 0.45%, 1.77%, 0.10% and 0.04%. Furthermore, a 1% increase in renewable energy consumption and agricultural productivity may result in reductions in CO2 emissions of 0.66% and 0.20%. This article put forward policy recommendations in the areas of low-carbon economy, promoting renewable energy use, sustainable urbanization, eco-friendly tourism, and climate-smart agriculture which would ensure environmental sustainability by reducing emissions in Mexico.

Greenhouse gases (GHGs) emissions, notably carbon dioxide (CO2) emissions are causing global climate change, which poses enormous hazards to human life, the environment, development, and sustainability. Bangladesh is predominantly an agricultural country experiencing continuous economic growth and rapid urbanization which is causing higher energy consumption and CO2 emissions. The present study empirically explores the nexus between economic growth, energy use, urbanization, agricultural productivity, and CO2 emissions in Bangladesh. Time series data from 1972 to 2018 were utilized by employing the Dynamic Ordinary Least Squares (DOLS) approach. The Autoregressive Distributed Lag (ARDL) bounds test revealed evidence of cointegration among the variables in the long run which has been verified by the Johansen cointegration test and Engle-Granger cointegration test. The empirical findings reveal that economic growth, energy use, urbanization, and reduced agricultural productivity increase CO2 emissions in Bangladesh. The estimated results are robust to alternative estimators such as fully modified least squares (FMOLS), and canonical cointegrating regression (CCR). In addition, the pairwise Granger causality test is utilized to capture the causal linkage between the variables. This study adds to the current body of knowledge by shining light on the factors that contribute to environmental degradation in Bangladesh. This article put forward policy recommendations aimed at sustainable development by establishing strong regulatory policy instruments to reduce environmental degradation.

Global climate change, exacerbated by greenhouse gas (GHG) emissions, notably carbon dioxide (CO2) emissions, provides huge risks to the environment, development, and sustainability. This study empirically investigated the dynamic impacts of economic growth, fossil fuel energy use, renewable energy use, and agricultural productivity on CO2 emissions in Nepal. Time series data from 1990 to 2019 were utilized by applying the autoregressive distributed lag (ARDL) bounds testing approach followed by the Dynamic Ordinary Least Squares (DOLS) method. The ARDL bounds test revealed evidence of cointegration among the variables. The DOLS findings revealed that an increase in economic growth and fossil fuel energy use by 1% for each variable would increase CO2 emissions by 0.61% and 0.67%, respectively. Conversely, a 1% increase in renewable energy use and agricultural productivity may lead to CO2 emissions reduction by 3.65% and 0.41% in the long run. The estimated results are robust to alternative estimators such as fully modified least squares (FMOLS) and canonical cointegrating regression (CCR). In addition, the pairwise Granger causality test was utilized to capture the causal linkage between the variables. This article put forward policy recommendations aimed at sustainable development by establishing strong regulatory policy instruments to reduce environmental degradation.

Global climate change caused by Greenhouse gasses (GHGs), particularly carbon dioxide (CO2) emissions, poses incomparable threats to the environment, development, and sustainability. This research investigates the dynamic impacts of economic growth, renewable energy use, and agricultural land expansion on CO2 emissions in Peru. Time series data from 1990 to 2018 were utilized applying the autoregressive distributed lag (ARDL) bounds testing approach followed by the Dynamic Ordinary Least Squares (DOLS) method. The DOLS estimate findings show that the coefficient of economic growth is positive and significant with CO2 emissions, indicating a 1% increase in economic growth is related to a 0.59% rise in CO2 emissions. Moreover, the coefficient of renewable energy use is negative and significant, implying that increasing renewable energy use by 1% results in a 0.52% reduction in CO2 emissions in the long run. Furthermore, the estimated long-run coefficient of agricultural land is positive and significant which reveals that agricultural land expansion by 1% is associated with an increase in CO2 emissions by 1.58% in the long run. The empirical findings reveal that economic growth and agricultural land expansion increase CO2 emissions while increased renewable energy use improves environmental quality by reducing CO2 emissions in Peru. The estimated results are robust to alternative estimators such as fully modified least squares (FMOLS) and canonical cointegrating regression (CCR). This article provides policy recommendations aimed at a low-carbon economy, promoting renewable energy use and climate-smart agriculture to achieve emission reduction and environmental sustainability.

Singapore is a foremost tourist destination country experiencing continuous economic growth and rapid urbanization which is causing higher energy consumption and carbon dioxide (CO2) emissions. This study aims to investigate the dynamic impacts of economic growth, energy use, urbanization, and tourism on CO2 emissions in Singapore. Time series data from 1990 to 2019 were utilized by employing the dynamic ordinary least squares (DOLS) approach. The DOLS findings show that the long-run coefficient of economic growth is negative and significant, indicating that a 1% rise in economic growth will result in a 0.99% reduction in CO2 emissions. Furthermore, the coefficient of energy use is positive and significant which reveals that an increasing 1% of energy use is linked with a rising of 0.52% CO2 emissions in the long run. In addition, the long-run coefficient of urbanization is positive and significant, implying that rising urbanization by 1% causes a 1.90% increase in CO2 emissions. Moreover, the coefficient of tourism is positive and significant, which specifies that an increase in tourism activities by 1% is associated with a 0.45% increase in CO2 emissions in the long run. The estimated results are robust to alternative estimators such as ordinary least squares (OLS), fully modified least squares (FMOLS), and canonical cointegrating regression (CCR). Furthermore, the pairwise Granger causality test was utilized to capture the causal linkage between the variables. This article put forward policy recommendations toward environmental sustainability by establishing strong regulatory policy instruments to reduce environmental degradation.

The study aims to investigate the dynamic impacts of economic growth, energy use, urbanization, agricultural productivity, and forested area on carbon dioxide (CO2) emissions in Kazakhstan. Time series data from 1996 to 2020 were utilized by employing the Dynamic Ordinary Least Squares (DOLS) approach. The Autoregressive Distributed Lag (ARDL) bounds test revealed evidence of cointegration among the variables in the long run which has been verified by the Johansen cointegration test and Engle-Granger cointegration test. The empirical findings revealed that a 1% increase in economic growth, energy use, and urbanization cause an increase in CO2 emissions by 0.14%, 0.81%, and 1.28% in Kazakhstan. Conversely, a 1% increase in agricultural productivity and the forested area may lead to CO2 emissions reduction by 0.34% and 2.59%, respectively in the long run. The estimated results are robust to alternative estimators such as fully modified least squares (FMOLS) and canonical cointegrating regression (CCR). In addition, the pairwise Granger causality test was utilized to capture the causal linkage between the variables. This article put forward policy recommendations in the areas of low-carbon economy, renewable energy, sustainable urban development, climate-smart agriculture,

Global climate change caused by Greenhouse gases (GHGs), particularly carbon dioxide (CO2) emissions, poses incomparable threats to the environment, development, and sustainability. This research investigates the potential of economic growth, renewable energy use, and technological innovation to achieve environmental sustainability by reducing CO2 emissions in Kazakhstan. Time series data from 1996 to 2018 were utilized by applying the Dynamic Ordinary Least Squares (DOLS) method. The DOLS estimate findings show that the coefficient of economic growth is positive and significant with CO2 emissions, indicating a 1% increase in economic growth is related to a 0.34% rise in CO2 emissions. Moreover, the estimated long-run coefficient of fossil fuel consumption is positive and significant which reveals that an increasing 1% of fossil fuel energy use is linked with a rising of 6.99% CO2 emissions. Furthermore, the coefficient of renewable energy use is negative and significant, which indicates that increasing renewable energy use by 1% is associated with CO2 emissions reduction by 0.38% in the long run. In addition, increasing technological innovation significantly lowers CO2 emissions, implying that a 1% increase in the number of patent applications is linked to a 0.15% reduction in CO2 emissions. The empirical findings reveal that economic growth and fossil fuel consumption increase CO2 emissions in Kazakhstan while increased renewable energy use and technological innovation help to achieve environmental sustainability by reducing CO2 emissions. This article provides policy recommendations aimed at a low-carbon economy, promoting renewable energy use, financing technological advancement, and environmental sustainability in Kazakhstan.

Malaysia has a large extent of forest cover that plays a crucial role in storing biomass carbon and enhancing carbon sink (carbon sequestration) and reducing atmospheric greenhouse gas emissions, which helps to reduce the negative impacts of global climate change. This article estimates the economic value of forest carbon stock and carbon value per hectare of forested area based on the price of removing per ton CO2eq in USD from 1990 to 2050. The economic value of biomass carbon stored in the forests is estimated at nearly USD 51 billion in 2020 and approximately USD 41 billion in 2050, whereas carbon value per hectare forest area is estimated at USD 2885 in 2020 and USD 2388 in 2050. If the BAU scenario of forest loss (converting forests to other land use) continues, the projected estimation of carbon stock and its economic value might fall until 2050 unless further initiatives on proper planning of forest management and ambitious policy implementation are taken. Instead, Malaysia’s CO2 emission growth started to fall after 2010 due to rising forest carbon sink of 282 million tons between 2011 and 2016, indicating a huge potential of Malaysian forests for future climate change mitigation. The estimated and projected value of carbon stock in Malaysian forest biomass, annual growth of forest carbon, forest carbon density and carbon sink would be useful for the better understanding of enhancing carbon sink by avoiding deforestation, sustainable forest management, forest conservation and protection, accurate reporting of national carbon inventories and policy-making decisions. The findings of this study could also be useful in meeting emission reduction targets and policy implementation related to climate change mitigation in Malaysia.

Over the past few decades, there has been a rapid change in forest and land cover, especially in tropical forests due to massive deforestation. The major factor responsible for the changes is to fulfill the growing demand of increasing population through agricultural intensification, rural settlements, and urbanization. Monitoring forest cover and vegetation are essential for detecting regional and global environmental changes. The present study evaluates the influence of deforestation on land surface temperature (LST) in the states of Kedah and Perak, Malaysia, between 1988 and 2017. The trend in forest cover change over the time span of 29 years, was analyzed using Landsat 5 and Landsat 8 satellite images to map the sequence of forest cover change. With the measurement of deforestation and its relationship with LST as an end goal, the Normalized Difference Vegetation Index (NDVI) was used to determine forest health, and the spectral radiance model was used to extract the LST. The findings of the study show that nearly 16% (189,423 ha) of forest cover in Perak and more than 9% (33,391 ha) of forest cover in Kedah have disappeared within these 29 years as a result of anthropogenic activities. The correlation between the LST and NDVI is related to the distribution of forests, where LST is inversely related to NDVI. A strong correlation between LST and NDVI was observed in this study, where the average mean of LST in Kedah (25 °C) is higher than in Perak (22.6 °C). This is also reflected by the decreased NDVI value from 0.6 to 0.5 in 2017 at both states. This demonstrated that a decrease in the vegetation area leads to an increase in the surface temperature. The resultant forest change map would be helpful for forest management in terms of identifying highly vulnerable areas. Moreover, it could help the local government to formulate a land management plan.