Sanitation, water, energy use, and traffic volume affect environmental quality: Go-for-green developmental policies

Using smart transportation assets to hedge fossil energy markets: Evidence from quantile-based VAR approach

This study explores the time-varying correlations and quantile spillover connectedness to identify the hedging potential of smart transportation assets for energy markets. The study finds that amid crises like COVID-19 and the Russia-Ukraine conflict, smart transportation indices demonstrate strong safe-haven characteristics against volatility in equity commodity energy and electricity transmission and distribution infrastructure indices. Additionally, the smart transportation, electric vehicle, and drone indices offer limited hedging benefits and safe-haven attributes for carbon emission allowance and natural gas. Furthermore, smart transportation assets are the major spillover transmitters to fossil energy assets across all quantiles. These outcomes hold substantial implications for environmental advocates, investors, and policymakers.

Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak

Loktak Lake, one of the largest freshwater lakes in Manipur, India, is critical for the eco-hydrology and economy of the region, but faces deteriorating water quality due to urbanisation, anthropogenic activities, and domestic sewage. Addressing the urgent need for effective pollution management, this study aims to assess the lake's water quality status using the water quality index (WQI) and develop advanced machine learning (ML) tools for WQI assessment and ML model interpretation to improve pollution management decision making. The WQI was assessed using entropy-based weighting arithmetic and three ML models - Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) - were optimised using a grid search algorithm in the H2O Application Programming Interface (API). These models were validated by various metrics and interpreted globally and locally via Partial Dependency Plot (PDP), Accumulated Local Effect (ALE) and SHapley Additive exPlanations (SHAP). The results show a WQI range of 72.38-100, with 52.7% of samples categorised as very poor. The RF model outperformed GBM and DNN and showed the highest accuracy and generalisation ability, which is reflected in the superior R2 values (0.97 in training, 0.9 in test) and the lower root mean square error (RMSE). RF's minimal margin of error and reliable feature interpretation contrasted with DNN's larger margin of error and inconsistency, which affected its usefulness for decision making. Turbidity was found to be a critical predictive feature in all models, significantly influencing WQI, with other variables such as pH and temperature also playing an important role. SHAP dependency plots illustrated the direct relationship between key water quality parameters such as turbidity and WQI predictions. The novelty of this study lies in its comprehensive approach to the evaluation and interpretation of ML models for WQI estimation, which provides a nuanced understanding of water quality dynamics in Loktak Lake. By identifying the most effective ML models and key predictive functions, this study provides invaluable insights for water quality management and paves the way for targeted strategies to monitor and improve water quality in this vital freshwater ecosystem.

Under the different sectors: the relationship between low-carbon economic development, health and GDP

Developing a modern low-carbon economy while protecting health is not only a current trend but also an urgent problem that needs to be solved. The growth of the national low-carbon economy is closely related to various sectors; however, it remains unclear how the development of low-carbon economies in these sectors impacts the national economy and the health of residents. Using panel data on carbon emissions and resident health in 28 province-level regions in China, this study employs unit root tests, co-integration tests, and regression analysis to empirically examine the relationship between carbon emissions, low-carbon economic development, health, and GDP in industry, construction, and transportation. The results show that: First, China's carbon emissions can promote economic development. Second, low-carbon economic development can enhance resident health while improving GDP. Third, low-carbon economic development has a significant positive effect on GDP and resident health in the industrial and transportation sector, but not in the construction sector, and the level of industrial development and carbon emission sources are significant factors contributing to the inconsistency. Our findings complement existing insights into the coupling effect of carbon emissions and economic development across sectors. They can assist policymakers in tailoring low-carbon policies to specific sectors, formulating strategies to optimize energy consumption structures, improving green technology levels, and aiding enterprises in gradually reducing carbon emissions without sacrificing economic benefits, thus achieving low-carbon economic development.

Economic growth and carbon emissions in Pakistan: the effects of China's Logistics Industry

The logistics business is a crucial contributor to economic development, yet it is also the leading source of carbon emissions. Economic growth at the expense of environmental deterioration is a challenging issue; this phenomenon offered a new avenue for scholars and policymakers to investigate and address these issues. The recent study is one of the attempts to explore this intricate subject. The goal of this research is to determine whether or not the Chinese logistics sector has an impact on Pakistan's GDP and carbon emissions as a result of CPEC. The research utilized data from 2007Q1 to 2021Q4 using the ARDL approach for an empirical estimate. Due to the mixed order of variable integration and finite data set, the ARDL technique is well deserved, which helps reach sound policy inferences. The study's key results indicated that China's logistic business enhances Pakistan's economic development and carbon emissions in the short and long term. Similarly, China's energy usage, technology, and transportation contribute to Pakistan's economic progress at the price of environmental damage. The empirical study may be a model for other developing nations, given Pakistan's viewpoint. With the support of the empirical results, policymakers in Pakistan and other associated countries would be able to plan for sustainable growth in conjunction with CPEC.

How to Evaluate Green Development Policy Based on the PMC Index Model: Evidence from China

Implementing green development is important to realizing a harmonious relationship between humans and nature, and has attracted the attention of governments all over the world. This paper uses the PMC (Policy Modeling Consistency) model to make a quantitative evaluation of 21 representative green development policies issued by the Chinese government. The research finds: firstly, the overall evaluation grade of green development is good and the average PMC index of China's 21 green development policies is 6.59. Second, the evaluation of 21 green development policies can be divided into four different grades. Most grades of the 21 policies are excellent and good; the values of five first-level indicators about policy nature, policy function, content evaluation, social welfare, and policy object are high, which indicates that the 21 green development policies in this paper are relatively comprehensive and complete. Third, most green development policies are feasible. In twenty-one green development policies, there are: one perfect-grade policy, eight excellent-grade policies, ten good-grade policies, and two bad-grade policies. Fourthly, this paper analyzes the advantages and disadvantages of policies in different evaluation grades by drawing four PMC surface graphs. Finally, based on the research findings, this paper puts forward suggestions to optimize the green development policy-making of China.