Ever since the emancipation of a country, its environmental quality has undergone a significant transition during the development phases; Bangladesh is no exception. Bangladesh is facing a serious threat in the age of global warming, and climate change as the country is looking forward in achieving the SDGs by 2030. Yet, there is a dearth of study regarding the relationship among crucial macroeconomic drivers and ecological footprint (a proxy for environmental degradation). Under the circumstances, this study explores the effects of economic growth, capital formation, urbanization, trade openness, energy use, and technological innovation on the ecological footprint by adopting the novel dynamic Autoregressive Distributed Lag (ARDL) simulations approach for Bangladesh, using annual frequency data from 1972 to 2017. Empirical results from the bounds test ascertained that there exists a long-run equilibrium association among the outlined variables. Furthermore, the novel dynamic ARDL simulation results revealed that Bangladesh is yet to achieve the environmental Kuznets curve (EKC) hypothesis. It was observed that the Bangladesh economy is still at the scale stage of its economic trajectory, emphasizing economic growth relative to her environmental status. However, capital formation, urbanization, and energy use seemed to degrade environmental quality, while trade openness and technological innovation upgraded the environmental quality. Putting it more elaborately, a unit escalation in GDP per capita increases the ecological footprint by 0.829% in the long run, while a unit increase in energy consumption upsurges the ecological footprint by 1.074% and 0.761% in the long run and short run, respectively. As regards technology innovation, one unit increase in it cutbacks the ecological footprint by 0.596% in the long run. Furthermore, the frequency domain causality unveiled the long-run feedback effect between economic growth and ecological footprint. The study further presents possible recommendations that can sustainably address environmental issues, keeping the economy buoyant.

Measuring the risks of the carbon financial market is of great significance for investment decision-making, risk supervision, and the healthy development of the carbon trading market. Different from previous studies based on traditional VaR (value at risk), this study measures the integrated risk of China’s carbon market based on the Copula-EVT (Extreme Value Theory) -VaR model which can explore the unique strength of the copula and EVT-VaR models, of which the copula model is applied to capture the dependence between the different risk factors of carbon price volatility and macroeconomic fluctuation, while the EVT-VaR is used to explore the risk value. The empirical results show that the traditional VaR that only considers a single risk factor from carbon price volatility is likely to overestimate the risk. In addition, compared with other methods that do not consider the interdependence between risk factors, using the copula function to measure the carbon market integration risk is more effective, and backtesting also confirms this conclusion. This paper provides a specific reference for carbon emission companies to participate in the carbon market. It provides a theoretical basis for the supervision of the risk management of the carbon market.

The construction of green finance index is a three-tiered process that involves macroeconomic, ecological, and monetary indicators. Therefore, this research is one of the first thorough assessments of the impacts of green financing regulations in China, examining 30 provinces during the period of 2010 to 2017. Data envelopment analysis models for 30 provinces in China have been tested by using non-radial models and longitudinal datasets. The findings demonstrate that between 2010 and 2017, the efficiency of China’s provinces such as Beijing, Fujian, Guangdong, Hainan, Hebei, Jiangsu, Liaoning, Shandong, Shanghai, Tianjin, and Zhejiang and provinces of central regions as well as western provinces of country green economies has increased with distinct geographical disparities becoming more apparent. The geographical distribution of economic efficacy in the green economy is greatest in the eastern parts and poorest in the Chongqing, Gansu, Guangxi, Guizhou, Inner Mongolia, Ningxia, Qinghai, Shaanxi, Sichuan, Tibet, Xinjiang, and Yunnan. The study revealed that sustainable financial growth may be accomplished via the creation of green financing, which can be achieved by employing different solutions across the macroeconomic, institutional, and ecological considerations. The western and central areas, however, have a significant negative association. There are substantial variations in factors at the state and federal level from the viewpoint of dependent variables. Eventually, the research offers some suggestions for future ecological impact of China, along with the creation of new environmental legislation.

Poverty is a significant global ongoing issue that influences a substantial amount of people despite all efforts to eliminate or lessen it. Although poverty is an eminent problem because of its negative consequences, many people are unaware of the concept of poverty. Poverty not only degrades the human capacity but also undermines economic growth in developing regions. This research aims at exploring the impact and heterogeneity of agricultural factor endowment investment on rural relative poverty. By using the data obtained from China household tracking survey (CFPS-2018), probit model approach was applied to analyze the relationship between agricultural factor endowment investment and rural relative poverty. Finally, the intermediary effect of the selected model was validated through the robustness test. The study findings showed that the input of agricultural factor endowment can alleviate the relative poverty in rural areas of China. Our study findings also revealed that there is a significant heterogeneity in family size, endowment type of agricultural technology elements and the agricultural type. A positive association between the investment in agricultural technology and land factor endowment with relative poverty alleviation by increasing the total value of cash and deposits was revealed from the results. The overall findings of this study provide useful insights to facilitate government institutions to stabilize the agricultural labor force, accelerate the process of agricultural modernization and provide careful consideration to land property rights.

In China, the county is not only an important component of industrial areas and a large contributor of carbon emissions, but also a key administrative unit for the implementation of carbon peak and carbon neutrality goals and policies. The spatiotemporal variations and structural characteristics of carbon emissions at the county scale are of great significance to China’s dual goals of regional carbon policy implementation and low carbon spatial planning. Thus, it is important and insightful to conduct an in-depth and detailed examination of these characteristics while focusing on a typical iron and steel industry county-level city in North China. This study systematically calculated the carbon emissions of the county-level city of Wu’an from 2008 to 2017, and explored their structural characteristics and spatiotemporal variations. The results showed that (1) under the influence of macroeconomic and national policies, the carbon emissions of county-level cities dominated by the iron and steel industry show obvious phased characteristics; (2) there is a significant negative correlation between industry carbon emission concentrations and industrial carbon emissions; (3) within the steel industry system, sintering, iron smelting, steelmaking, and metal product processing are the main sources of carbon emissions, and the coal-based production process of the iron and steel industry needs a fundamental reformation; and (4) the carbon emission of Wu’an City shows obvious spatial differentiation characteristics. The geographic distribution of carbon emissions in Wu’an City is very unbalanced and tended to cluster together in urban areas, industrial and mining areas, and major towns. Taking 2014 as the turning point, the spatial pattern of carbon emissions in Wu’an City presents different variation characteristics.

Following the United Nations Sustainable Development Goals (UN-SDGs), which place emphasis on relevant concerns that encompass access to energy (SDG-7) and sustainable development (SDG-8), this research intends to re-examine the relationship between urbanization, CO₂ emissions, gross capital formation, energy use, and economic growth in South Korea, which has not yet been assessed using recent econometric techniques, based on data covering the period between 1965 and 2019. The present study utilized the autoregressive distributed lag (ARDL), dynamic ordinary least square (DOLS), and fully modified ordinary least squares (FMOLS) methods, while the gradual shift and wavelet coherence techniques are utilized to determine the direction of the causality. The ARDL bounds test reveals a long-run linkage between the variables of interest. Empirical evidence shows that CO₂ emissions trigger economic growth. Thus, based on increasing environmental awareness across the globe, it is necessary to change the energy mix in South Korea to renewables to enable the use of sustainable energy sources and establish an environmentally sustainable ecosystem. Moreover, the energy-induced growth hypothesis is validated. This result is supported by the causality analysis, which shows a one-way causality running from energy consumption to GDP in South Korea. This suggests that South Korea cannot embark on conservative energy policies, as such actions will damage economic progress. Additionally, a unidirectional causality is seen from CO₂ emissions and energy consumption to economic growth. These findings have far-reaching consequences for GDP growth and macroeconomic indicators in South Korea.

This study attempts to construct an econometric model using China’s natural disaster losses and macro-industry development data from 1980 to 2017 to explore the macroeconomic fluctuations caused by natural disasters. The structural vector autoregressive (SVAR) and the seemingly unrelated regression (SUR) models are employed in estimating the impact of natural disasters on China’s macroeconomy and how the disasters specifically affect the three sectors of the economy: primary, secondary, and tertiary. This study concludes that even though natural disasters in China do not significantly affect the overall real GDP, they have adverse impacts on the production in the primary industry, causing a sudden reduction in the means of production in the market and directly affecting various industries, but the impact on the secondary and tertiary industries is weak. This study also shows that the effect of natural disasters on the primary sector reduced significantly following industry restructuring after China’s accession to the World Trade Organization (WTO). The impact of natural disasters on the primary industry could be reduced by adjusting the industrial structure to deal with macroeconomic shocks caused by natural disasters in order to promote macroeconomic stability of both regional and national economies. Finally, national aid policy should focus on the primary industry since that sector is significantly affected by natural disasters shocks.

The paper examines the impact of macroeconomic variables on CO2 emissions, very few research studies are available to estimate the asymmetric impact and causality. Because of the significance of asymmetries, this paper examines the asymmetric impact of economic growth, crude oil use, and FDI inflows on CO2 emissions in the India wherein COP (Crude oil price) is comprised as the extra variable. The implicate aggressive growth of selected variables over the period 1990–2020 is also assessed. This study uses the methodology ARDL and NARDL model to determine the macroeconomics variable's effects on CO2 emission over the period 1990-2020. Additionally, also applied the EKC (Environmental Kuznets Curve) hypothesis with an application of ARDL and NARDL model. With help of the ARDL and NARDL model, the study shows the results that a rise in economic growth would reduce CO2 (carbon dioxide) emissions while a decrease in economic growth would raise CO2 emissions which indicates an inverted U-shaped Curved relationship between economic growth and CO2 emissions. The positive and negative shockwaves in COP (crude oil prices) have a satisfactory and substantial impact on CO2 emissions as well. Besides, the crude oil consumption with positive shockwave confirmations has a positive and substantial impact on CO2 emission. In addition, the results of FDI inflows support the pollution heaven hypothesis. In light of these outcomes, this paper also recommended policy implications and future research, the policy implications are where the descending flow of FDI allows limited space to India in FDI selection; however, the existence of emission merging and implementation of carbon pricing may facilitate India in achieving its environmental targets.

The intensification of international trade movements and economic interconnectivity has far-reaching implications for many macroeconomic indicators, not to mention ecological consequences. To this end, this analysis examines the dynamic interaction between foreign direct investment (FDI), natural resources, economic advancement, and urbanization on consumption-based carbon emission which is adjusted to global trade for oil-producing Sub-Saharan Africa countries. The time frame for this analysis is from 1990 to 2018. To examine the nature of relationship between the outlined variables, a balanced panel econometric analysis alongside augmented mean group (AMG), common correlated effect mean group (CCEMG), and the Driscoll-Kraay(DK) OLS techniques while the system-GMM was utilized for robustness purposes. The outcomes reveal that income increases consumption-based carbon emission within the range of 0.668 to 1.1333%; natural resources also increase consumption-based carbon emission within the range of 0.0159 to 0.2304%; FDI on the other hand increases consumption-based carbon emission around 0.0156 to 0.186%, while urbanization increases consumption-based carbon emission within the range of 0.0231 to 0.6176% in the long run. Thus, there is a positive relationship between consumption-based carbon emission and all the understudied variables within the oil-producing Sub-Sahara Africa countries thereby affirming the pollutant haven hypothesis for the countries on the premises that foreign direct investment inflow has a detrimental influence on the receiving economies alongside natural resource. Hence, the outcomes suggest the need to pursue low-carbon strategies for a cleaner and friendly environment.

Clarifying the time-varying spillovers among pilot carbon emission permit trading markets in China is an important foundation for building the national carbon emission trading market. We calculate the dynamic spillover of carbon price return among the pilot carbon emission permit trading markets in China with the time-varying connectedness approach. The dataset is constructed from transaction data from seven pilot carbon markets in China during the period of June 23, 2014, to December 31, 2020. The quantitative analysis suggests that (i) Beijing and Chongqing carbon emission trading markets are the main spillover markets of carbon price returns, with strong pricing power, while the Guangdong and Tianjin markets are the main receivers of the price return spillover in other pilot carbon emission trading markets. (ii) The spillover effect among China’s carbon markets has a strong policy orientation. The improvement and development of the carbon market driven by macroeconomic regulation and control policies can effectively improve the spillover ability of the carbon market, and the market trading activity, namely the volatility of the carbon price return rate, can amplify the spillover ability of the carbon market in the short term. (iii) There exist three types of price return spillover among China’s pilot carbon emission trading markets, including central divergence, one-way chain transmission, and circular spillover. Along with the improvement of market operation efficiency, the central divergent type of spillover shifts to the pattern of circular spillover. It is necessary for the government to improve market efficiency and ensure the coordinated development of China’s pilot carbon emission trading market and national carbon emission trading market.