The success of sustainable development heavily depends on successful energy transition toward renewable or carbon-free energy sources. This study attempted to analyze the impact of sustainable development and environmental initiatives on sustainable energy transition in selected OECD economies. For this purpose, the study generated the dataset of environment air and GHG emission, environmental-related technologies, development (gross domestic product, trade openness, and gross domestic spending on R&D) and sustainable environment (air and GHG emission and environmental-related technologies), and energy sources (renewable energy consumption, nonrenewable energy consumption, and sum of total energy consumption) of selected economies of OECD between 2000 and 2020. This study utilized dynamic panel GMM for regression analysis, and FMOLS and DOLS were applied as the robustness models. Empirical results indicated that sustainable development and a sustainable environment contribute positively to the energy transition process in OECD economies. However, these factors also negatively contribute to non-renewable energy consumption in OECD. Thus, the study’s outcomes confirmed the sustainable energy transition in OECD. Therefore, this study suggested that the success of Sustainable Development Goals depends on successful energy transition.

In recent years, the rapid development of electric vehicles has gained widespread attention, and has especially brought new vitality to the fast-growing logistics service industry. Electric vehicles in urban logistics are not only ideal transportation tools but also do not affect the environment. This study thus implements the economic valuation of electric vehicles in urban logistics from the viewpoints of both manufacturers and business users through the investigation of pricing mechanism and discusses the potential for improving policy-making together with the real case in China. The net present value (NPV) approach is used to quantitatively analyze the investment decisions of urban logistics electric vehicles, with government regulation, incentive policy, and carbon emission trading taken into consideration. Our findings provide insights into the decision-making mechanism for commercializing electric vehicles in urban logistics that involves optimizing the government subsidy and transaction price between the manufacturers and business users. The results imply that with rationalized government policy incentives and coordinated transaction price, both manufacturers and business users are expected to achieve their break-even in limited time periods. The carbon cap placed on business users rather than manufacturers would be relatively more conducive to the marketization process.

Two bentonites (sodic bentonite, BSN, and calcic bentonite, BCN) were treated with nitric acid to improve the adsorption capacities using ciprofloxacin (CIP) as an adsorbate. The results demonstrated that nitric acid treatment enhances the specific area and increases the pore volume of both bentonites. The mechanism of adsorption of CIP was investigated using a kinetic model, isotherms, and pH influence. The adsorption capacity for BSN increased from 294.1 mg.g−1 to 416.6 mg.g−1 after acid treatment, while the increase in the adsorption capacity of BCN was minor. The increase in CIP removal may be due to an increase in the specific area and the presence of acidic surface functional groups. The adsorption mechanism of CIP on all adsorbents was governed by external diffusion, internal diffusion, and adsorption to reach equilibrium. The adjustment of strong adsorbents to the BET model indicated multilayer adsorption. The best adsorbent is sodic bentonite with nitric acid treatment. The study of the pH effect showed that the mechanism of CIP adsorption is hydrogen bonds and electrostatic interaction. Therefore, the acid treatment was found promising to improve the adsorbent characteristic of clays for removing CIP from water.

Deforestation is the accelerating factor of climate change in developing countries. The German Watch Report 2020 had rated Pakistan number seventh most affected country due to adverse impacts of climate change. The problem of deforestation poses an existential danger to the forest-depleted country. It is of utmost importance to predict the main drivers to control deforestation. This study was conducted from October 2021 to August 2022 in dry temperate forests of the Chilas to investigate the current condition, causes of deforestation, and predicted the main drivers by using a binary regression model. Stratified random sampling techniques and fixed area plot method were used and taken ground measurements during field inventory to access current situation of deforestation. While a non-probability quota sampling technique and semi-structured questionnaire were utilized for the determination of main drivers of deforestation through respondent’s survey. The forest inventory result showed that most trees fall in immature and sub-mature (mainly in 10–20 and 20–30 cm) diameter classes while the binary logistic regression model predicted dominating four primary drivers (unsustainable fuel wood extraction, unsustainable timber extraction and urban crawling and rural expansion/habituation, and free and uncontrolled livestock grazing) and one secondary driver (wood for energy needs). To address the underlying causes of deforestation, the government must supply alternate energy sources, as well as other economic possibilities to reduce dependency on forests.