High delicate particulate matter (PM₂.₅) concentration can seriously reduce air quality, destroy the environment, and even jeopardize human health. Accordingly, accurate prediction for PM₂.₅ plays a vital role in taking precautions against upcoming air ambient pollution incidents. However, due to the disturbance of seasonal and nonlinear characteristics in the raw series, pronounced forecasts are confronted with tremendous handicaps, even though for seasonal grey prediction models in the preceding researches. A novel seasonal nonlinear grey model is initially designed to address such issues by integrating the seasonal adjustment factor, the conventional Weibull Bernoulli grey model, and the cultural algorithm, simultaneously depicting the seasonality and nonlinearity of the original data. Experimental results from PM₂.₅ forecasting of four major cities (Shanghai, Nanjing, Hangzhou, and Hefei) in the YRD validate that the proposed model can obtain more accurate predictive results and stronger robustness, in comparison with grey prediction models (SNGBM(1,1) and SGM(1,1)), conventional econometric technology (SARIMA), and machine learning methods (LSSVM and BPNN) by employing accuracy levels. Finally, the future PM₂.₅ concentration is forecasted from 2020 to 2022 using the proposed model, which provides early warning information for policy-makers to develop PM₂.₅ alleviation strategies.

Water pollution is an urgent problem that needs to be controlled via green transformation and the development of the Yangtze River Economic Belt (YREB). Based on the water pollutant discharge and socio-economic database of prefecture-level cities in the YREB from 2011 to 2015, this study explores the spatiotemporal variations in water pollutant discharge in the YREB via two main indicators: chemical oxygen demand (COD) and ammonia nitrogen (NH₃–N). Further, the spatial effects and determinants of water pollutant discharge are quantitatively estimated. The results show that (1) the water pollutant discharge in the YREB has decreased significantly, with the COD and NH₃–N discharge reduced by 10.46% and 10.79%, respectively, and the discharge reduction in the lower reaches was the most prominent; (2) the spatial pattern of water pollutant discharge in the YREB was generally stable and partially improved, and cities with a high rate of water pollutant reduction in the YREB were distributed in the main stream region of the Yangtze River and the intersection of the main stream and tributaries; (3) spatial effects had a significant impact on water pollutant discharge in the YREB, with regional cooperation and economic radiation through environmental management and control initially showing a combined reduction trend in regional water pollutants; and (4) determinants of population size and agricultural economic share declined to varying degrees at the end of the study period, although the urbanization level continued to increase, indicating that urbanization in the YREB occurred too quickly and that water pollutant discharge reduction was limited. However, economic development leading to the deterioration of the water environment was alleviated. In addition, foreign direct investment (FDI) inflows and rapid industrialization processes must be monitored to increase the reduction in characteristic water pollutants.

The impact of surface ozone pollution on winter wheat yield is empirically estimated by considering socio-economic and weather determinants. This research is the first to use an economic framework to estimate the ozone impact, and a unique county-level panel is employed to examine the impact of the increasing surface ozone concentration on the productivity of winter wheat in China. In general, the increment of surface ozone concentration during the ozone-sensitive period of winter wheat is determined to be harmful to its yield, and a conservative reduction of ozone pollution could significantly increase China's wheat supply.

There has been an increasing interest in the pollution caused by meso- and microplastics (MMPs) in terrestrial ecosystems. Mulch film was once considered to be the most important source of MMPs in the mulching cultivated soil. However, the academic community has not given sufficient scientific evidence. In this study, stratified random sampling method was used to selectively interview households in Hebei province, China (400 households, 20 villages, 5 counties). Finally, household characteristics and mulch film use behavior of 41 households were collected, and corresponding soil samples were sampled. The results showed that 1) the abundance of MMPs was 29.3 ± 33.1 items·kg⁻¹ (DW) and the particle size of MMPs was 2.95 × 10³±1.75 × 10³ μm, and the proportion of MMPs derived from Polyethylene (PE) was only 18.8%; 2) the mass of MMPs was 2.90 ± 3.72 mg kg⁻¹ (DW) and the proportion of PE MMPs was 43.75%, which has the highest mass percentage; 3) After controlling the endogenous and dummy variables, the use history of mulch film (HistMF) was found to be positively correlated to the abundance of MMPs and inversely correlated to the particle size, but nor with the mass of MMPs; 4) Regarding the heterogeneous characteristics of MMPs, including particle size, color, shape, and type, the findings found the absence of a significant correlation between HistMF and the abundance and mass of PE. In summary, mulch-derived MMPs are not the primary source of MMPs in the mulching cultivated soil in terms of abundance but probably be in terms of mass.

Numerous experiments have demonstrated reductions in the yields of cereal crops due to tropospheric O3, with losses of up to 25%. However, the only British econometric study on O3 impacts on winter wheat yields, found that a 10% increase in AOT40 would decrease yields by only 0.23%. An attempt is made here to reconcile these observations by developing AOT40 maps for Great Britain and matching levels with a large number of standardised trial plot wheat yields from many sites over a 13-year period. Panel estimates (repeated measures on the same plots with time) show a 0.54% decrease in yields and it is hypothesised that plant breeders may have inadvertently selected for O3 tolerance in wheat. Some support for this is provided by fumigations of cultivars of differing introduction dates. A case is made for the use of econometric as well as experimental studies in prediction of air pollution induced crop loss.

Nitrogen and phosphorus loads are considered a major reason for the eutrophication of the Baltic Sea. Until now, most of the abatement has been made at point sources while the implementation of policies for nonpoint sources has not led to equally large reductions in emissions. The purpose of this study is to investigate the determinants of how nutrient abatement measures are implemented by countries in the agricultural sector of the Baltic Sea region. We investigate how goal setting, policy instrument choice, and the level of implementation is determined by characteristics of the abatement measure as well as socio-economic characteristics of the country where it is implemented. Econometric analysis of a cross-sectional data set suggests that income, institutional capacity, and economies of scope in abatement and enforcement are important determinants of policies developed and their implementation.

Driven by the buoyancy of economy and continuous improvement of people's living standards, residential sector has gradually become the second largest CO2 emissions source in China. Reducing the fast rising rate of CO2 emissions in this sector is essential for realizing the target of carbon emission mitigation in China. The researches on the driving factors of residential CO2 emissions have attracted scholars' attention recently, yet few studies can interpret the causality relationship between household per capita income-expenditure-CO2 emissions at national and regional levels. Based on econometric techniques and a panel data set, this paper presents an investigation of the causality relationship, which combines household per capita income, per capita expenditure and per capita CO2 emissions (hereafter referred to as PI, PE, and CE, respectively) on a national level and within three regions (namely, eastern, central, and western regions of China) from 1997 to 2014. Urban and rural areas are considered as well. The empirical results manifest a varied causality relationship in different regions. For example, PI and PE correspond to CE in eastern rural area, but this phenomenon does not occur in central rural area. In addition, urban and rural differences are displayed. There is no causality between PI and PE in western urban area, while a bidirectional causal relationship emerges in PI and PE for western rural area. Finally, this study proposes some policy implications to decrease the increase rate of household CO2 emissions in China.

This study quantifies the impact of financial inclusion and export diversification in attaining the target of green growth for SAARC economies during the period 2000 to 2019. For the analysis purpose, this study employed second-generation econometric techniques that deal with heterogeneity and cross-sectional dependence issues. To this end, CUP-FM and CUP-BC are used to investigate the long-run dynamic equilibrium relationship among the variables of interest. The outcomes show that financial inclusion and institutional quality are eco-friendly variables and play a vital role in attaining green growth. In contrast, export diversification and FDI are inversely related with green growth in SAARC economies. Furthermore, a unidirectional causality running from financial inclusion to green growth and financial inclusion to export diversification is observed. On the basis of investigated outcomes, this research suggests essential policy recommendations to attain green growth.

There are growing concerns about environmental degradation and economic expansions in West Africa. Although there are several growth-environmental studies in Africa, there is limited empirical research exploring West African countries’ potential of benefiting from the environmental Kuznets curve (EKC) hypothesis, with the few studies on this subject reporting diverse results based on selected West African countries. To fill this gap, this study explored the relationship between economic growth and environmental degradation within the EKC framework using 16 West African countries sub-grouped into low-income countries (LICs) and lower-middle-income countries (LMICs) between 1990 and 2018. This study implemented second-generation panel econometric estimators that are robust to cross-sectional dependent and parameter heterogeneity. The empirical results revealed that the data is cross-sectionally dependent, heterogeneous, integrated of order one, 1(1), and cointegrated. Controlling for other environmental determinants, panel estimates from the Augmented Meant Group and Common Correlated Effect Mean Group estimators revealed that economic growth accelerates environmental degradation in West African countries, with a greater impact on LMICs, followed by LICs in West Africa. The results also showed that West African countries especially LMICs could benefit from the EKC hypothesis. On the other hand, growth-environmental degradation among LICs in West Africa shows a monotonous increasing relationship. We found strong evidence to support for feedback hypothesis between economic growth and environmental degradation in LMICs, LICs, and West Africa as a whole. Based on the findings, policy recommendations that consider both LMICs and LICs and West Africa as a whole were offered to policymakers.

At present, China’s economy is transforming from rapid development to high-quality development, and the prominent problems in the industrial structure need to be resolved urgently. The implementation of the green credit policy restricts loans to industries with high energy consumption, high pollution, and overcapacity to prevent excessive expansion and consumption of resources, thereby achieving the goals of industrial restructuring and environmental protection. Technological innovation is an important means to cope with environmental changes and adjust the industrial structure. Can technological innovation under green credit achieve the upgrading of the industrial structure? This paper combines green credit, technological innovation, and industrial structure upgrading to conduct research, and theoretically analyzes the impact of green credit and technological innovation on industrial structure upgrading, and analyzes how green credit affects industrial structure upgrading through technological innovation. An empirical test using panel data from 30 provinces in China from 2005 to 2017 revealed that green credit can promote the upgrading of the industrial structure. Econometric analysis based on the mediation effect model found that green credit not only directly promotes the upgrading of the industrial structure, but also has the mediation effect that affects the upgrading of the industrial structure through technological innovation. In the sub-regional regression analysis, technological innovation affects the upgrading of the industrial structure. The mediation effect is still significant. Based on the research in this article, it is recommended to actively implement green credit policies, expand the scale of green credit, firmly implement the innovation-driven development strategy, and at the same time vigorously develop clean energy and reduce energy consumption. The research significance of the article is to explore the impact of green credit on industrial structure upgrading, which has implications for the implementation and promotion of subsequent green credit policies. At the same time, it enriches the research on green credit.