Concerns regarding environmental sustainability have generally been an important element in achieving long-term development objectives. However, developing countries struggle to deal with these concerns, which all require specific treatment. As a result, this study explores the interaction between financial development, renewable energy consumption, technological innovations, and CO₂ emissions in India from 1980 to 2019, taking into account the critical role of economic progress and urbanization. The Autoregressive Distributed Lag (ARDL) model is used to quantify long-run dynamics, while the Vector Error Correction Model is used to identify causal direction (VECM). According to the study’s conclusions, financial development has a considerable positive impact on CO₂ emissions. The coefficient of renewable energy consumption and technical innovations, on the other hand, is strongly negative in both the short and long run, indicating that increasing these measures will reduce CO₂ emissions. Furthermore, economic expansion and urbanization have a negative impact on environmental quality since they emit a significant amount of CO₂ into the atmosphere. The results of the robustness checks were obtained using the Fully Modified Ordinary Least Squares (FMOLS), the Dynamic Ordinary Least Squares (DOLS), and the Canonical Cointegration Regression (CCR) approaches to verify the findings. The VECM results reveal that there is long-run causality in CO₂ emissions, financial development, renewable energy utilization, and urbanization. A range of diagnostic tests were also used to confirm the validity and reliability. This study delivers new findings that contribute to the existing literature and may be of particular interest to the country’s policymakers in light of the financial system and its role in environmental issues.

Establishing a fair platform for allocating carbon emission responsibility worldwide determines the sustainability and efficiency of the world’s climate policy and framework. In the context of global environmental load displacement and CO₂ transfer, this paper endeavors to examine the relationship between economic complexity and embodied carbon emissions based on cross-country panel data. Our study utilizes the generalized method of moments (GMM) approach to estimate our dynamic models covering 34 OECD countries and 24 non-OECD countries from 1995 to 2015. The empirical results show a heterogeneous impact of economic complexity on embodied carbon emissions in exports (EEE) and imports (EEI). Besides, the scale effect, composition effect, and technology effect are also significant drivers of embodied carbon emissions. The improvement of economic complexity can decrease the marginal effects of export scale and export structure on foreign EEE (but not domestic EEE) significantly, while the marginal positive impacts of technology on EEE can be further enhanced by economic complexity growth. Moreover, there is no strong evidence to prove the significant indirect impacts of economic complexity on foreign carbon emission embodied in imports, while economic complexity has significantly positive indirect impacts on domestic carbon emission embodied in imports only through import scale. In the subsample regressions, we found asymmetric impacts of economic complexity between high-income countries and low- and middle-income countries.

Since the opening up of China, its manufacturing sector contributed significantly toward economic development on one hand and caused elevated environmental pollution and heavy resource consumption problems on the other hand. Green productivity is an essential means of addressing environmental problems and promoting sustainable environmental performance and long-term economic growth. When the factors affecting the green total factor energy productivity (GTFEP) are studied in recent years, the hitherto literature lacks the relationship between GTFEP, environmental regulations, and global value chain (GVC) participation. Using GTFEP as a proxy of environmental upgrading for the Chinese manufacturing industry, this study investigates the nexus between the GTFEP, environmental regulation index (ERI), and total production length index (TPLI). Based on panel data 18 industries from 2000 to 2014, the current study employed the panel pool mean group estimators for the auto-regressive distributed lag model (ARDL) and estimated the long-run relationship between variables. Findings revealed the positive effect of ERI and GVC participation (TPLI) on the GTFEP of the Chinese manufacturing industry. Furthermore, pairwise panel causativeness analysis indicated unidirectional causality between GTFEP and ERI, running from GTFEP to ERI. Based on research findings, some conclusions and policy implications are derived, such as strict enforcement of environmental regulations, industry-specific policies, promotion of clean energy resources, and acceleration of the country’s further opening-up and reforms. All of these will help promulgate the GTFEP and environmental upgrading of the Chinese manufacturing sector, thus leading to sustainable economic development decoupled from environmental pollution.