Particulate Matter (PM₂.₅) pollution in China has been a primary concern for public health in recent years, which requires banks to appropriately control their credit supply to industries with high pollution, high energy consumption, and surplus capacity. For this reason, this paper examines economic determinants of PM₂.₅ concentrations and incorporates the spatial spillover effect of bank credit by employing the spatial Durbin model (SDM) under the stochastic impacts by regression on population, affluence and technology framework. Using China's provincial dataset from 1998 to 2016, the main findings are as follows: First, there is evidence in support of spatial dependence of PM₂.₅ concentrations and their inverted U-shaped relationship with economic growth in China. Second, PM₂.₅ concentrations in a province tend to increase as the level of its own urbanization increases, but they decrease as its own human capital and bank credit increase. Meanwhile, the level of neighboring urbanization positively influences a province's PM₂.₅ concentrations, whereas neighboring population size, industrialization, trade openness, and bank credit present negative impacts. Third, indirect effects of the SDM indicate significant and negative spatial spillover effect of bank credit on PM₂.₅ concentrations. These findings implicate policies on reforming economic growth, urbanization, human capital and bank credit to tackle PM₂.₅ pollution in China from a cross-provincial collaboration perspective.

In recent years, ozone pollution has become more and more serious in China. Several epidemiological studies have demonstrated the correlation between short-term ozone exposure and several health risks including all-cause mortality, cardiovascular mortality, and respiratory mortality. In this study, the daily ozone exposure levels with 10 km × 10 km resolution were estimated based on satellite data derived from Ozone Monitoring Instrument (OMI) and the monitoring data. The health impacts for potential decrease in the daily ozone concentration and the corresponding economic benefits in 2016 were estimated by applying the environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. By reducing the daily maximum 8-h average concentration of ozone to 100 μg/m³, the estimated avoided all-cause mortalities were 120 × 10³ (95% confidence interval (CI): 67 × 10³, 160 × 10³) cases and the correspondingly economic benefits ranged from 36 to 64 billion CNY using amended human capital (AHC) and willingness to pay (WTP) method in 2016. If the daily maximum 8-h average concentration of ozone were rolled back to 70 μg/m³, the estimated avoided all-cause mortalities were 160 × 10³ (95% CI: 98 × 10³, 230 × 10³) cases and economic benefits ranged from 54 to 95 billion CNY based on AHC and WTP methods.

Black carbon (BC) exposure in China continues to be relatively high, prompting researchers to assess BC exposure levels using data from monitoring sites, satellite remote sensing, and models. However, data regarding the application of a combined strategy comprising the analysis of monitoring data and various types of data to simulate BC exposure levels are lacking. Hence, the current study seeks to estimate short- and long-term BC exposure levels by combining national monitoring data with data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2). Furthermore, this study attempts to improve the spatio-temporal resolution of BC exposure levels using Bayesian maximum entropy (BME). The BME model performed well in terms of estimating short- (R² = 0.74 and RMSE = 1.76 μg/m³) and long-term (R² = 0.76 and RMSE = 1.3 μg/m³) exposure. Premature mortalities and economic losses were also assessed by applying localised concentration–response coefficients simulated in China. A total of 74,500 (95% confidence interval (CI): 23,900–124,500) and 538,400 (95% CI: 495,000–581,300) all-cause premature mortality cases were found to be associated with short- and long-term BC exposure, respectively. Meanwhile, short-term BC exposure was associated with economic losses ranging from 7.5 to 13.2 billion US dollars (USD) (1 USD = 6.36 RMB on January 19, 2022) based on amended human capital (AHC) and willingness to pay (WTP), accounting for 0.06%–0.1% of China's total gross domestic product (GDP) in 2017 (1.2 × 10⁴ billion USD), respectively. The economic losses for long-term exposure varied from 53 to 93.2 billion USD based on AHC and WTP, accounting for 0.4%–0.8% of China's total GDP in 2017, respectively.

Along with the rapid socioeconomic development, air pollution in China has become a severe problem. One component of air pollution, in particular, PM2.5 has aroused wide public concern because of its high concentration. In this study, data were collected from over 900 monitoring sites of the newly constructed PM2.5 monitoring network in China. The interpolation methods were used to simulate the PM2.5 exposure level of China especially in rural areas, thus reflecting the spatial variation of PM2.5 pollution. We calculated the health benefit caused by PM2.5 in China in 2014 based on Environmental Benefits Mapping and Analysis Program (BenMAP), assuming achievement of China National Ambient Air Quality Standard (No. GB3095-2012). By reducing the annual average concentration of PM2.5 to the annual Grade II standard (35 μg/m3), the avoided deaths for cardiovascular disease, respiratory disease and lung cancer could reach 89,000 (95% CI, 8000–170,000), 47,000 (95% CI, 3000–91,000) and 32,000 (95% CI, 6000–58,000) per year using long term health function, respectively. The attributable fractions of cardiovascular disease, respiratory disease and lung cancer to all cause were 42%, 22% and 15%, respectively. The total economic benefits for rolling back the concentration of PM2.5 to the level of 35 μg/m3 were estimated to be 260 (95%CI: (73, 440) billion RMB and 72 (95%CI: (45, 99) billion RMB using willingness to pay (WTP) and human capital (HC) methods, respectively, which account for 0.40% (95%CI: (0.11%, 0.69%) and 0.11% (95%CI: (0.07%, 0.15%) of the total annual Gross Domestic Product (GDP) of China in 2014.

Some cities in China are facing serious air pollution problems including high concentrations of particles, SO2 and NOx. Exposure to PM2.5, one of the primary air pollutants in many cities in China, is highly correlated with various adverse health impacts and ultimately represents a cost for society. The aim of this study is to assess health impacts and external costs related to PM2.5 pollution in Beijing, China with different baseline concentrations and valuation methods. The idea is to provide a reasonable estimate of the total health impacts and external cost due to PM2.5 pollution, as well as a quantification of the relevant uncertainty. PM2.5 concentrations were retrieved for the entire 2012 period in 16 districts of Beijing. The various PM2.5 related health impacts were identified and classified to avoid double counting. Exposure-response coefficients were then obtained from literature. Both the value of statistical life (VSL) and the amended human capital (AHC) approach were applied for external costs estimation, which could provide the upper and lower bound of the external costs due to PM2.5. To fully understand the uncertainty levels, the external cost distribution was determined via Monte Carlo simulation based on the uncertainty of the parameters such as PM2.5 concentration, exposure-response coefficients, and economic cost per case. The results showed that the external costs were equivalent to around 0.3% (AHC, China's guideline: C0 = 35 μg/m3) to 0.9% (VSL, WHO guideline: C0 = 10 μg/m3) of regional GDP depending on the valuation method and on the assumed baseline PM2.5 concentration (C0). Among all the health impacts, the economic loss due to premature deaths accounted for more than 80% of the overall external costs. The results of this study could help policymakers prioritizing the PM2.5 pollution control interventions and internalize the external costs through the application of economic policy instruments.

The last decades have shown that human activities damaged environmental quality and biodiversity. As accepted the need of sustainable development, there should be balance between economic, social and environmental issues in the long term. Ecological footprint is an important indicator that shows how human activities reduce environmental quality among a specific region or country. In addition, human capital is mostly used indicator to estimate economic and environmental development level. In this study, it is aimed to explore the effect of human capital on fishing footprint for 10 Mediterranean Countries during the period 1995–2018. According to empirical evidences, it is concluded that for relatively low human capital level, human capital has negative effect on sustainability and in the relatively high level, it has positive effect on sustainability.

The oceans play a crucial role in the global environment and the sustainability of human populations, because of their involvement in climate regulation and provision of living and non-living resources to humans. Maintenance of healthy oceans in an era of increasing human pressure requires a high-level understanding of the processes occurring in the marine environment and the impacts of anthropogenic activities. Effective protection and sustainable resource management must be based, in part, on knowledge derived from successful research. Current marine research activities are being limited by a need for high-quality researchers capable of addressing critical issues in broad multidisciplinary research activities. This is particularly true for developing countries which will require the building of capacity for marine scientific research. This paper reviews the current activities aimed at increasing marine research capacity in developing and emerging countries and analyses the challenges faced, including: appropriate alignment of the research goals and societal and policy-relevant needs; training in multidisciplinary research; increasing capacity for overall synthesis of scientific data; building the capacity of technical staff; keeping highly qualified personnel in marine scientific research roles; cross-cultural issues in training; minimising duplication in training activities; improving linkages among human capital, project resources and infrastructure. Potential solutions to these challenges are provided, along with some priorities for action aimed at improving the overall research effort.

Making nature’s value visible to humans is a key issue for the XXI century and it is crucial to identify and measure natural capital to incorporate benefits or costs of changes in ecosystem services into policy. Emergy analysis, a method able to analyze the overall functioning of a system, was applied to reckon the value of main ecosystem services provided by Posidonia oceanica, a fragile and precious Mediterranean seagrass ecosystem. Estimates, based on calculation of resources employed by nature, resulted in a value of 172€m−2a−1. Sediment retained by meadow is most relevant input, composing almost the whole P. oceanica value. Remarks about economic losses arising from meadow regression have been made through a time-comparison of meadow maps. Suggested procedure represents an operative tool to provide a synthetic monetary measure of ecosystem services to be employed when comparing natural capital to human and financial capitals in a substitutability perspective.

The relations among ecological footprints, biocapacity per capita, gross domestic product per capita, natural resources, urbanization, human capital, and hydroelectric consumption are analyzed from 1971Q1 to 2017Q4 for Brazil and China. The novel quantile autoregressive distributive lag method was employed to analyze the long-run and short-run dynamics of environmental degradation. The findings revealed that economic growth has a positive role in the environmental degradation of both countries. However, human capital, natural resources, and hydropower have heterogeneous effects across quantile distribution and between the two countries. Based on the quantile dynamics of environmental degradation, the present study mentions policy implications for sustainable development.

This study investigates the effects of human capital, bio-capacity, energy use, and economic growth on the ecological footprint of G20 countries for the period 1970–2016, using the panel dynamic common correlated effects (DCCE) model. In the study, the G20 was considered in two groups, as developed and emerging economies. According to the DCCE estimation results, the long-term impact of human capital on the ecological footprint is negative and statistically strong in the developed economies while it is insignificant in the emerging economies. The impact of bio-capacity on the ecological footprint is positive in the short and long term in the emerging economies, and only in the short term in the developed economies. In addition, economic growth and energy use undermine the environmental quality in both groups of countries. The error correction coefficients are negative and statistically significant, which means that the deviations from the short-term equilibrium converge the long-term equilibrium level for both groups.