Solar PV (photovoltaic) technology has gained considerable attention worldwide, as it can help reduce the adverse effects of CO₂ emissions. Though the government of Pakistan is adopting solar PV technology due to its environmental friendliness nature, studies focusing on consumer’s acceptance of solar PV are limited in the country. This research aims to close this knowledge gap by looking into the various considerations that may influence consumers’ willingness to adopt (WTA) solar PV for household purposes. The study further contributes by expanding the conceptual framework of the theory of planned behavior by including three novel factors (perceived risk, perception of self-efficacy, and openness to technology). The analysis is based on questionnaire data collected from 683 households in Pakistan’s provincial capitals, including Lahore, Peshawar, Quetta, Gilgit, and Karachi. The proposed hypotheses are investigated using the state-of-the-art structural equation modeling approach. The empirical results reveal that social norms, perception of self-efficacy, and belief about solar PV benefits positively influence consumers’ WTA solar PV. On the contrary, the perceived risk and solar PV cost have negative effects. Notably, the openness to technology has an insignificant effect. This study can help government officials and policymakers explore cost-effective, risk-free technologies to lessen the environmental burden and make the country more sustainable. Based on research results, study limitations, as well as prospective research directions, are also addressed.

The Paris agreement is a unified arrangement for the global response to climate change and entered into force on 4 November 2016. Its long-term goal is to hold the global average temperature rise well below 2 °C. China is committed to achieving carbon neutrality by 2060 through various measures, one of which is green technology innovation (GTI). This paper aims to analyze the levels of GTI in 30 provinces in mainland China between 2001 and 2019. It uses the spatial econometric models and panel threshold models along with the slack based measure (SBM) and Global Malmquist-Luenberger (GML) index to analyze the spatial spillover and nonlinear effects of GTI on regional carbon emissions. The results show that GTI achieves growth every year, but the innovation efficiency was low. China’s total carbon dioxide emissions were increasing at a marginal rate, but the carbon emission intensity was declining year by year. Carbon emissions were spatially correlated and show significant positive agglomeration characteristics. The spatial spillover of GTI plays an important role in reducing carbon dioxide emissions. In the underdeveloped regions in China, this emission reduction effect was even more significant.

In recent years, China has gradually become one of the countries with the largest levels of foreign direct investment (FDI). FDI has played a significant role in promoting Chinese economic development, and the FDI technology spillover effect is one of the core forces driving China towards reaching new growth milestones. Therefore, due to the country’s interest in development, there is competition for FDI throughout China. However, the existing imperfect environmental protection system cannot prevent FDI from flowing into China’s highly polluting and resource-intensive industrial chain, possibly causing serious environmental problems. Therefore, the topic of properly introducing foreign capital to promote development and effectively end China’s current environmental pollution crisis has become a research focus. To explore FDI’s impact on China’s economic growth, technological innovation, and environmental pollution, we use Chinese provincial panel data for 2004–2016 to construct a dynamic panel simultaneous-equation model. Considering the interrelationships between the equations, we construct economic models of economic growth, technological innovation, and pollution emissions, and incorporate them into the same research system for generalized method of moments (GMM) estimation. Our results show that FDI has a significant and positive direct impact on China’s economic growth and technological innovation, and can furthermore have a significant pull effect on the domestic economy through the backward spillover channel. At the same time, FDI has a direct and significant impact on the increase in regional waste-water discharge, while exhibiting a pollution halo effect on the emission of sulfur dioxide (SO₂) and chemical oxygen demand (COD) emissions directly. In addition, we observe “benign feedback mechanism” between technological innovation output and these three types of pollution, namely SO₂ emission, COD emissions, and ammonia and nitrogen discharge.

The increasingly serious destruction of the natural environment represents a great threat to the sustainable development of human beings and the earth. Under pressure from the government and public opinion, companies must assume environmental responsibility; however, there is no conclusion on whether corporate environmental responsibility is beneficial to companies. From the perspective of investment efficiency, this paper collects panel data from Chinese listed companies from 2011 to 2016 to discuss the impact of corporate environmental responsibility on investment efficiency and the moderating role of the institutional environment and consumer environmental awareness. The results show that corporate environmental responsibility can significantly positively affect investment efficiency, but this effect is not a short-term effect; it needs time to play a role. Second, in regions with a good institutional environment, corporate environmental responsibility has a more significant impact on improving investment efficiency. Finally, with the improvement of consumer environmental awareness, companies that assume environmental responsibility can address underinvestment. The research in this paper supports stakeholder theory, indicating that corporate environmental responsibility is not “selfless dedication”. In addition, the research results of this paper are robust and not subject to endogenous influences.

The China State Council promulgated the “Culture Industry Promotion Plan” on 26 September 2009, raising the status of the culture industry to that of a strategic industry. This paper applies a three-stage data envelopment analysis (DEA) model to investigate the efficiency of investments made in the culture industry in 2011 in China. The results show that the overall efficiency of the culture industry in China is still at a relatively low level. The scale of companies in the culture industry is a key factor restricting development. The external environment has a great influence on this efficiency; the efficiency gap between the eastern, central and western areas is obvious and reflects the degree of the environmental impact on the culture industry in these regions. The western region will experience progress if the environment changes. In contrast, the central and eastern regions will see less progress. This paper proposes the following corresponding policy recommendations based on the analysis. (1) China should focus on expanding the scale of culture industry enterprises. (2) We need to accelerate the market-oriented reform of cultural institutional mechanisms. (3) We should pay attention to the efficiency gap between cultural industries in the eastern, central and western areas. We should use government power to support the development of cultural industries in the western region.

The increasing adoption of electric vehicles (EVs) and the growing reliance on renewable energy sources underscore the urgent need for accurate forecasting of EV charging demand to support the development of sustainable and resilient energy systems. This study proposes an explainable machine learning (ML)-based approach to predict hourly EV charging demand using a high-resolution dataset from California, spanning January 2021 to May 2024. Five ML models—XGBoost, random forest, LightGBM, CatBoost, and linear regression—were evaluated, with XGBoost achieving the highest predictive accuracy. Scenario analysis revealed a strong positive relationship between renewable energy penetration and EV charging demand: 10%, 20%, and 30% increases in renewable usage led to 20%, 33%, and 47% increases in predicted demand, respectively. SHAP-based feature importance analysis identified renewable energy usage, carbon footprint reduction, and grid stability as key drivers of charging behavior. The proposed framework offers a scalable, interpretable, and data-driven solution to support the alignment of EV charging infrastructure with decarbonization goals. By linking renewable energy integration with demand-side dynamics, the findings offer actionable insights for the design of adaptive electricity pricing strategies and sustainable mobility policies, contributing to the broader vision of low-carbon, environmentally responsible transportation systems.