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The digital economy (DE) is emerging as a crucial driver of economic growth and an effective tool for alleviating resource and environmental pressures, thereby evolving into a significant force in facilitating green transformation. This study elaborates on the theoretical mechanism of the impact of DE on green total factor productivity (GTFP), and conducts multidimensional empirical tests using panel data from 284 cities in China. The main findings are as follows: (1) DE exerts significant positive direct, indirect, and spatial spillover effects on GTFP, signifying its growing role as a robust driver of GTFP. Notably, technological innovation emerges as a key mediator of DE’s impact on GTFP. (2) The impact of DE on GTFP exhibits a distinct pattern: initially pronounced, gradually diminishing, and then rebounding as DE progresses. (3) DE tends to exacerbate, rather than alleviate, the development divide and resource curse, especially in underdeveloped and resource-rich cities where its benefits are constrained. (4) Government behavior is pivotal in influencing DE’s impact on GTFP. Supportive policies and strict environmental regulations are critical in harnessing DE’s positive contributions to GTFP. This study lays a scientific foundation for leveraging the “green attributes” of DE and offers insights into bridging the developmental disparities among cities.

The heterogeneity of investor sentiment plays a key role in causing the asymmetry of information transmission patterns and transmission intensity between markets. This paper analyzes the asymmetric risk spillover between the international crude oil market and other markets, including commodity market and financial market, using monthly data from June 2006 to October 2020. The risk from the international crude oil market is separated into upside and downside risks. The empirical results suggest that, first, from the perspective of static spillover, the risk spillover between the international oil market and commodity market or financial market enhances significantly in response to rising return; second, from the perspective of dynamic spillover, the asymmetric risk spillover of international crude oil market manifests the key roles played by important events happening in the crude oil market and alternating attributes of crude oil. Some policy suggestions are proposed in light of these empirical results.

The construction industry is an important material production sector of the national economy, and trade in goods and services between different industrial sectors in different regions may result in the transfer of embodied carbon emissions from the construction industry. A systematic identification of the relationships and structural characteristics of the embodied carbon transfer in the construction industry is crucial for rationally defining the responsibility for emission reduction and scientifically formulating emission reduction policies to promote the effective promotion of China’s carbon emission reduction actions. Based on the calculation of input-output theory, this study constructs a multi-regional input-output (MRIO) model of 31 provinces in China containing 28 industries to estimate the carbon emissions of the construction industry in 2017, it also combines the complex network theory to construct the industrial and regional embodied carbon transfer network of China’s construction industry, and calculates the network structure indexes to deeply explore the spatial transfer network structure characteristics of the embodied carbon transfer between regions of China’s construction industry in 2017. The results show that the construction, energy and building materials manufacturing sectors are at the core of the sectoral carbon transfer network structure, with strong network control. The embodied carbon transfer network between regions in the construction industry has a small-world character, more than 40% of all relevant regions have carbon transfer relationships with other regions, significant carbon emissions are transferred from the resource-rich, industrially well-endowed central-western and north-eastern provinces to the economically developed south-eastern coastal provinces. According to the results of the study, differentiated carbon emission reduction plans are formulated, and policy suggestions for optimizing the carbon emission reduction plan of the construction industry are put forward.

Prediction of bond return is a classic problem in financial area, providing an important basis for portfolio construction and risk management. The sustainable investment attribute of green bonds has been favored by investors, so that green bonds have become an important component for major asset allocation. However, due to the specific investment focus of green bonds, investors’ return expectations are influenced not only by traditional corporate bond factors, but also by related factors such as climate change and energy transition. Against the backdrop of increasingly severe climate risks and the global energy crisis, this paper analyses the volatility characteristics of China’s green bonds at multiple time scales, and introduces exogenous variables such as returns of the alternative financial assets, climate risks and returns of energy markets for prediction. Based on the LSTM model, the volatility of green bond yield at different time scales is separately predicted using optimal exogenous variable before integration. It is found that the new integrated prediction model can significantly improve the forecasting performance compared to traditional single LSTM models and simple decomposition-integrated models. Further, both climate risks and energy markets variables have a significant improvement effect on predicting green bond in low-frequency item, while energy markets variables also have a better predictive effect on trend items. Building on the use of only LSTM model, it could be further enhanced by integrating more algorithms to select the best single model for each component, further improve the prediction accuracy and provide a more effective quantitative tool for investment decision-making and risk management in related fields.

High energy intensity and energy consumption structure are the main contradictions restricting China’s green economic growth. Green technology innovation is an important factor to alleviate the energy dilemma. Referring to the IPC Green Inventory launched by the World Intellectual Property Organization, the green patents of 30 provinces in China from 2004 to 2019 are screened. Based on the panel data of 30 provinces in China, this study empirically tests the impact of green technology innovation on energy intensity and energy structure by employing fixed effect model and quantile regression model. The empirical results show that green technology innovation can significantly reduce energy intensity and energy structure, and the long-term effect is obviously higher than the short-term effect. Compared with the green utility model patent representing general innovation, the green invention patent representing disruptive innovation expresses more momentous long-term and short-term effect. The sustainable effect of green technology innovation will gradually weaken with the reduction of energy intensity and energy structure. For provinces with low energy structure, the impact of green technology innovation is no longer significant. Instrumental variable method and robustness test prove that the conclusion of this study is robust. This study provides support for the government and enterprises to promote technological innovation and play a part in alleviating the energy dilemma.

As is glaringly apparent, the world is now bedeviled with environmental challenges and ecological complications. This may not be unconnected with the persistent ecological challenges emanating from fossil fuel consumption and changing lifestyles. This is why various attempts are ongoing to advance the course of renewable energy and economic growth as a response to environmental change. Therefore, this study seeks to investigate the dynamic effect of external debt, energy usage, and real income on the ecological footprint in Turkey, covering the period 1985–2017. To achieve the stated objective, the study used the newly developed Bootstrap Autoregressive Distributed Lag (BARDL) test and the Granger causality test to unravel the co-movement as well as the direction of causality among the variables. The results revealed that external debt influences environmental quality in both the short and the long run. However, energy consumption and real income have been found to have retarded environmental quality in both the short and the long run. The Granger causality test results revealed that the causal flow direction runs from external debt, economic growth, and energy usage to environmental quality. Therefore, the study argues that for the Turkish government to achieve a long-term plan of environmental sustainability, there is a need for the government to venture into debt consolidation programs such as implementing tax increases and cutting public spending in order to increase fiscal space that would finance long-term environmental protection policies.

Introduction: More than 16% of the total electricity used worldwide is met by hydropower, having local to regional environmental consequences. With positive indicators that energy is becoming more broadly available and sustainable, the world is moving closer to achieving Sustainable Development Goal 7 (SDG 7). Pakistan became the first nation to include the Sustainable Development Goals (SDGs) in its national development strategy.Methodology: The current study sought to investigate the structural limits of Environmental Impact Assessment (EIA) guidelines for hydropower development in Pakistan. The study included the document review of the EIA reports about hydropower projects in Pakistan, scientific questionnaires from decision-makers, and public consultation.Results and Discussion: The document evaluates that an adequate mechanism is available, and donors like the Asian Development Bank and World Bank observe the implementation process of EIA in Pakistan. However, a comprehensive analysis of the EIA system found several things that could be improved, not only in the institutional framework but also in actual implementation and practices. More than 20% of respondent decision-makers disagreed with the compliance of the current Institutional Framework with EIA guidelines, and 25% think that the existing guidelines followed in Pakistan are not aligned with international standards and practices for Hydropower in actual practice. EIA has a limited impact on decision-making due to insufficient technical and financial resources.Recommendations: There should be a think tank with experts who can meet the needs of present and future epochs. The public should be communicated with and educated about EIA. For better efficiency, the officers and decision-makers should be trained internationally, i.e., the Water and Power Development Authority (WAPDA).

The economy of Pakistan has constantly been plunged due to its severe electricity shortages over the last 2 decades and persistently faces challenges in revamping its electricity supply network. The purpose of this research was to assess the causal relationship between carbon dioxide emissions (CO2), combustible renewable and waste (CRW), electric power consumption (EC), electricity production from coal (EPC), hydroelectric (EPH) and natural gas (EPN) sources, energy use (EU) and gross domestic product (GDP). The scope of this research included Pakistan’s annual time series data from 1971 to 2014. This study employed Autoregressive Distributed Lag (ARDL) bound testing analysis to determine the long-term and short-term correlations among all research parameters. This research also conducted Augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests to evaluate the stationarity existence among dependent variable and independent variables. The outcomes of the fully modified least squares (FMOLS), dynamic ordinary least square (DOLS) and canonical co-integrating regression (CCR) estimators showed that coefficients of EC, EPH and GDP all were a significantly positive relationship with CO2 emissions, while the coefficients of CRW, EPC and EU were negatively significant, respectively. Furthermore, the outcomes from the short-run analysis revealed that the error correction term value was -0.8668, which indicates that from short-run to long-run equilibrium, the adjustment of the deviation of CO2 emission is by 86.68 percent annually. Moreover, the diagnostic results also demonstrated that the model employed in this research is stable and reliable. Pakistan was selected in this research work because of the deficit of power and if environmental degradation continues unchecked, it will eventually affect the state’s economic growth and CO2 emissions. The study’s primary policy recommendation is that government energy policymakers in Pakistan who create the environment framework in should pursue conservative energy measures as such measures will not negatively impact economic growth.