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Primarily, the current study seeks to explore the influence of financial development, economic growth, trade openness, non-renewable and renewable energy utilization on carbon dioxide (CO2) emission in Pakistan from 1990 to 2017. The findings of structural break unit root tests indicated that a few variables are stationary at the level and others integrated at the first difference. The bound F-test and Johansen cointegration tests confirmed the evidence that a long-run relationship exists among concerned variables. The long-run results explore that financial development and renewable energy penetration significantly accelerate the environmental quality, while economic growth, non-renewable energy utilization, and trade openness are responsible for deteriorating the environmental quality. The results from the short-run estimate explore that non-renewable energy utilization and trade openness significantly reduce the environmental quality while renewable energy sources are beneficial for environmental quality. Moreover, the current study discovered the unidirectional Granger causality relationship from economic growth, trade openness, non-renewable and renewable energy penetration to carbon emission in Pakistan. The outcomes of this study provide some insightful policy suggestions to overcome the detrimental effect of environmental degradation.

In recent years, financial development, trade policies, and energy performance have attracted attention due to their behavior on environmental quality. Therefore, the current study examines the impact of financial development, trade openness, primary and renewable energy utilization, and economic growth on the ecological footprint in South Asian Association for Regional Cooperation (SAARC) countries from 1990 to 2017. This article progresses the proficiency of financial development by utilizing the comprehensive and multidimensional index of financial sector development based on their depth, access, and efficiency of their financial institutions and markets. In order to estimate the robust results, this study employed the cross-sectional dependency tests that allow the second-generation unit root, Westerlund cointegration, augmented mean group (AMG), error correction model (ECM), and Dumitrescu–Hurlin (D-H) panel non-causality tests. The results revealed a very weak effect of financial development in a panel of SAARC countries, while country-specific results reveal that financial development significantly enhances the pollution level in the case of Bangladesh and Sri Lanka. However, it improves the environmental quality in Nepal. Furthermore, trade openness only improves the environmental quality in the case of Nepal. Additionally, the findings explore that primary energy consumption enhances the ecological footprint in Bangladesh, Nepal, and Sri Lanka and reduces in case of Bhutan. On the contrary, renewable energy consumption significantly improves the environmental quality in all countries except Bangladesh. Finally, consistent with these findings, a number of suitable policy implications are expressed in the angle of SAARC economies.

The main purpose of this study is to explore the dynamic association between financial development, tourism, primary and renewable energy utilization, urbanization, and carbon emission by employing the longitudinal data of 52 countries from 1995 to 2017. Empirical results of panel pooled mean group-autoregressive distributive lag (PMG-ARDL) model reveal that financial development significantly improves the environmental quality in developed countries. However, it has a detrimental but insignificant effect on the environment in developing countries. In the case of developed countries, the profound tourism sector is more harmful to the environment due to a large number of tourist arrivals in contrast to the developing countries. There is a wide difference between developed and developing countries concerning industrial, regional, and economic structure, in the effect of financial and tourism development on carbon emission, but both urbanization and primary energy utilization promote carbon emissions. The utilization of renewable energy sources improves the environmental quality in both regions. Generally, it is suggested that investment in renewable energy resources in both regions affects pollution differently and still has the potential to accelerate environmental quality. Moreover, the panel causality test explores that there exists bidirectional causality between financial development, primary energy, and carbon emission in both regions, while a unidirectional causality is observed from urbanization to carbon emission in developed countries. In developing countries, it exists from tourism to carbon emission and carbon emission to renewable energy. Finally, from policy perspectives, the results of this research recommend developing the financial system, and more funds should be allocated in modern and eco-friendly energy projects and utilized energy-efficient technologies.

The pursuit of global carbon neutrality necessitates addressing the dual challenge of enhancing solar energy utilization while improving thermal comfort in high-rise residential areas, particularly in Yulin, northern Shaanxi, China, where abundant solar resources exist but maximizing solar acquisition often compromises summer thermal environment quality. This resource-comfort contradiction highlights the need for balanced architectural strategies in regions with pronounced seasonal variations. Building morphological parameter optimization is crucial for balancing annual solar energy capture against summer overheating risks, yet research remains insufficient. This study developed parametric layout models using Rhino-Grasshopper, considering key parameters including building length, width, height, density, floor area ratio, and south-facing angle deviation. Multi-objective optimization was conducted using NSGA-II genetic algorithm under regulatory constraints, while combining traditional regression analysis with convolutional neural networks (CNN) to investigate the influence mechanisms of these morphological parameters. Results indicate that the optimized building morphology can increase annual solar radiation acquisition (SRA) by 2.57% while maintaining comfortable summer Universal Thermal Climate Index (UTCI) values, effectively balancing solar energy capture and outdoor thermal comfort. Regression analysis revealed a positive correlation between building length and summer UTCI (r = 0.73), whereas CNN identified a negative correlation (−0.45). Both methods identified similar parameter combinations affecting SRA, with CNN demonstrating superior capability in capturing complex non-linear relationships. These findings provide evidence-based design guidelines specific to Yulin while offering implications for sustainable residential development in similar climates, advancing the integration of climate-adaptive design strategies.

• Chilling restrains the distribution of mangroves. We tested whether foliar phosphorus (P) fractions and gene expression are associated with cold tolerance in mangrove species. • We exposed seedlings of six mangrove populations from different latitudes to favorable, chilling and recovery treatments, and measured their foliar P concentrations and fractions, photochemistry, nighttime respiration, and gene expression. • A Kandelia obovata (KO; 26.45°N) population completely and a Bruguiera gymnorhiza (Guangxi) (BGG; 21.50°N) population partially (30%) survived chilling. Avicennia marina (24.29°N), and other B. gymnorhiza (26.66°N, 24.40°N, and 19.62°N) populations died after chilling. Photosystems of KO and photosystem I of BGG were least injured. During chilling, leaf P fractions, except nucleic acid P in three populations, declined and photoinhibition and nighttime respiration increased in all populations, with the greatest impact in B. gymnorhiza. Leaf nucleic acid P was positively correlated with photochemical efficiency during recovery and nighttime respiration across populations for each treatment. • Relatively high concentrations of nucleic acid P and metabolite P were associated with stronger chilling tolerance in KO. Bruguiera gymnorhiza exhibited relatively low concentrations of organic P in favorable and chilling conditions, but its partially survived population showed stronger compensation in nucleic acid P and Pi concentrations and gene expression during recovery.

Due to geographical constraints, island regions at edge distribution networks generally face challenges of resource shortages and high carbon emissions. To enhance resource utilization efficiency, this paper proposes a multi-energy utilization module (MEUM) for distributed-level island integrated energy systems (IES). The module efficiently recovers and utilizes secondary resources generated during system operation, thereby providing additional economic benefits for the system. Furthermore, to incentivize system units to participate in carbon emission reduction, the incentive-penalty stepped carbon trading mechanism (IPSCTM) is introduced in the system operation stage, which enhances the willingness of units to engage in carbon trading and reduces carbon emissions. Meanwhile, the scheduling problem of island IES that simultaneously considers efficient resource utilization and carbon emission reduction involves numerous interrelated variables, where traditional optimization methods rely on accurate models or predictive information. Therefore, to avoid modeling and prediction, this paper proposes a model-free deep reinforcement learning (DRL) approach to deal with the island IES scheduling problem. To validate the effectiveness of the proposed island IES model and solution approach, simulations are conducted based on operational datas from a representative island in northern China. The simulation results demonstrate that the proposed model can significantly reduce both the total operational cost and carbon emissions. Moreover, the proposed solution approach outperforms other methods in terms of optimization effectiveness and computational time.

The primary energy outputs of waste-to-energy plants are electricity and steam. Small-scale waste incineration power plants in China currently face major challenges such as high construction costs, low energy conversion efficiency, and poor economic performance. To address the issues of low energy conversion efficiency and poor economic returns, this paper designs different comprehensive energy utilization schemes. Through calculation and analysis, it predicts their implementation effects, providing a reference for the construction and operation of small-scale waste incineration power plants.

Under the “dual carbon” goal, the mismatch between the intermittent nature of wind-solar power generation and the stable energy demand of oil wells hinders efficient green energy utilization in oilfields, leading to low green electricity consumption, high curtailment rates, and poor economic benefits. To address this challenge, an optimization approach for oil well operation scheduling is proposed, which couples photovoltaic power fluctuations with the characteristics of intermittent pumping technology. Firstly, a multiobjective optimization model was developed to minimize grid electricity consumption per unit of liquid production and maximize the share of green electricity. The on-off schedule was mapped into a constrained binary sequence using a run-length encoding to reducing the solution space. Non-dominated Sorting Genetic Algorithm II (NSGA-II) was improved to increase both accuracy and convergence speed by introducing: (1) dual-mode initialization strategy guided by historical operating schedules and photovoltaic fluctuations. (2) a key-gene-preserving crossover operator to retain high-matching time segments; and (3) a peak-valley-guided mutation strategy to enable dynamic pruning of the solution space and goal-directed optimization. Case studies showed that the proposed method doubled green electricity consumption under stable production conditions, reduced grid electricity consumption per unit liquid by 41.67%, improved computational efficiency by two to three orders of magnitude, and enhanced the solution accuracy by 26.69%, indicating strong practical applicability.

With rapid economic development and tremendous population growth, emerging economies are facing huge pressure to conserve energy and reduce emissions. Although governmental authorities have formulated numerous relevant policies, a considerable number of farmers have yet to actively take energy-saving measures. Consequently, it is imperative to investigate how institutions (i.e., environmental regulations and informal institutions) affect the clean energy utilization behaviors (CEUBs) of farmers (e.g., biomass energy, natural gas, and solar energy) for better aligning governance strategies. Drawing on institutional theory, we explore the underlying influencing mechanisms of the institutional constraints on farmers’ CEUBs using survey data collected from Central China. The double hurdle model results reveal that both environmental regulations (e.g., incentivizing regulations, binding regulations) and informal institutions (e.g., value orientation) have positive impacts on farmers’ adoption of CEUBs. Informal institutions were found to have stronger influences on farmers’ CEUBs than formal regulations. Interestingly, the interactions between two institutional constraints primarily have substitutionary effects on the natural gas and solar energy using behaviors of farmers. The interaction effects of the two institutional constraints on biomass energy using behavior are complementary. Consequently, this study sheds new light on guiding farmer CEUBs and better aligning formal and informal energy strategies.

Liquified natural gas (LNG) is a clean primary energy source that is growing in popularity due to the distance between natural gas (NG)-producing countries and importing countries. The large amount of cold energy stored in LNG presents an opportunity for sustainable technologies to recover and utilize this energy. This can enhance the energy efficiency of LNG regasification terminals and the economic viability of the LNG supply chain. The energy stored in LNG in the form of low temperatures is referred to as cold energy. When LNG is regasified, or converted back into its gaseous form, this cold energy is released. This process involves heating the LNG, which causes it to vaporize and release its stored energy. The current state-of-the-art techniques for LNG cold energy utilization, including power generation, air separation, traditional desalination, and cryogenics carbon dioxide (CO2) capture are discussed in this review. While most of the current LNG cold energy utilization systems are presented, potential future applications are also discussed. The commercialization of sustainable technologies, such as improvement strategies for LNG cold energy utilization, is becoming increasingly important in the energy industry.