Shared network and supply chain features for synergetic control of carbon and air pollutant emissions

Breathing in danger: Understanding the multifaceted impact of air pollution on health impacts

Air pollution, a pervasive environmental threat that spans urban and rural landscapes alike, poses significant risks to human health, exacerbating respiratory conditions, triggering cardiovascular problems, and contributing to a myriad of other health complications across diverse populations worldwide. This article delves into the multifarious impacts of air pollution, utilizing cutting-edge research methodologies and big data analytics to offer a comprehensive overview. It highlights the emergence of new pollutants, their sources, and characteristics, thereby broadening our understanding of contemporary air quality challenges. The detrimental health effects of air pollution are examined thoroughly, emphasizing both short-term and long-term impacts. Particularly vulnerable populations are identified, underscoring the need for targeted health risk assessments and interventions. The article presents an in-depth analysis of the global disease burden attributable to air pollution, offering a comparative perspective that illuminates the varying impacts across different regions. Furthermore, it addresses the economic ramifications of air pollution, quantifying health and economic losses, and discusses the implications for public policy and health care systems. Innovative air pollution intervention measures are explored, including case studies demonstrating their effectiveness. The paper also brings to light recent discoveries and insights in the field, setting the stage for future research directions. It calls for international cooperation in tackling air pollution and underscores the crucial role of public awareness and education in mitigating its impacts. This comprehensive exploration serves not only as a scientific discourse but also as a clarion call for action against the invisible but insidious threat of air pollution, making it a vital read for researchers, policymakers, and the general public.

Achieving synergy between carbon mitigation and pollution reduction: Does green finance matter?

The synergy between carbon mitigation and pollution reduction (CMPR) serves as the predominate catalyst for facilitating green transformation. Although the Green Finance Polit Policy (GFPP) shares a commitment to green development, its effectiveness in fostering the synergy of CMPR remains uncertain. Hence, we adopted the Difference-In-Difference (DID) model to investigate the effect of GFPP on the synergy between CMPR during 2009-2019. Initially, a composite synergy degree model is employed to assess the synergy between CMPR at the prefectural level. Our empirical findings demonstrate a notable contribution of GFPP in augmenting the synergy between CMPR. Robustness tests using Tobit model, PSM-DID, and placebo tests (temporal and regional) also confirm the stability of the findings. Furthermore, the study identifies three plausible mechanisms, including industry optimization, green innovation, and energy structure, which are likely to impact the aforementioned synergy. Empirical tests provide supportive evidence that substantiates the theoretical validity of these mechanisms, indicating that optimizing industry structure and enhancing the quality of green innovation can amplify the positive impact of green finance. Additionally, the impact of green finance policy in augmenting the synergy between CMPR exhibits greater prominence in central regions, non-resource-based cities, and non-key environmental protection cities. Our study also uncovers an additional finding that green finance has a detrimental effect on the quality of green innovation, thereby impeding the advancement of synergy between CMPR. In summary, our findings offer novel insights into promoting the synergistic relationship between CMPR by means of implementing green finance policy.

Performance Measurement of the Sustainable Supply Chain During the COVID-19 Pandemic: A real-life case study

This paper aims to introduce a framework to measure the sustainable performance of the supply chain (SC) during the COVID-19 pandemic. The SC stakeholders in this investigation are Suppliers, Production / Remanufacturing / Refurbishing Centers (Factories), Collection / Distribution Centers, Recycling / Landfill Centers, and Customers. The suggested sustainable supply chain (SSC) performance measurement included three pillars with 23 indicators. To evaluate the overall sustainability of the SC understudy, a composite index has been developed that combines all the indicators to reflect the sustainability performance of the SC. Four steps are involved in creating a composite index:1) measuring the value of indicators, 2) weighing indicators, 3) Using the normalization technique, and 4) Evaluating the overall SSC indicator. The real case in Iran is selected as an illustrative case. Our research contributions are: We suggested a novelty indicator of SSC to better show the economic, environmental, and social tradeoffs during the COVID-19 pandemic and lockdowns. We have found and measured the negative and positive impacts of COVID-19 on aspects of sustainability in SC. Based on the achieved data of the real case study, a numerical example is represented to explain how to calculate the composite index. The main contribution of this paper is the development of SSC indicators during the COVID-19 epidemic.

Characteristics Analysis and Identification of Key Sectors of Air Pollutant Emissions in China from the Perspective of Complex Metabolic Network

Presently, China is in a critical period of economic transformation and upgrading. At the same time, it is also facing the pressure of serious atmospheric environmental pollution, which seriously threatens human health and hinders the sustainable economic development. Air pollutants are closely related to economic sectors, which together constitute a complex network. Air pollutants form an input–output ecological metabolic relationship among different sectors. Therefore, from the perspective of complex metabolic network, this study first constructs an environmental input–output model and then comprehensively uses the relevant methods of ecological network analysis and complex network analysis to analyze the characteristics of China’s air pollutant emission system. Secondly, the key joint sectors of NO_x and PM emissions are determined from the supply side and the demand side, respectively. Finally, the corresponding emission reduction measures are proposed for the identified key sectors.

A Big Data-Based Commuting Carbon Emissions Accounting Method—A Case of Hangzhou

Commuting carbon emissions are an essential component of urban carbon emissions, and determining how to reduce them is an area of great debate among researchers. The current research lacks a tool and instrument that can extensively account for residents’ commuting. Traditional methods are mainly based on questionnaire surveys, which have low accuracy at spatial and temporal aspects. High accuracy carbon emission accounting methods can effectively assist urban planning and achieve precise urban emissions reductions. This study applies a taxi commuting carbon emissions accounting method divided into two main steps. Firstly, the carbon emissions of taxi trajectories are calculated using taxi trajectory data and a carbon emission calculation method developed based on VSP. Secondly, the taxi trajectory and POI data are used to filter the commuter trajectory with the help of a two-step moving search method. In this way, the taxi commuting carbon emissions were obtained. Then, the spatial distribution characteristics of residential taxi commuting carbon emissions are analysed by spatial autocorrelation tools, which could facilitate low carbon zoning management. A typical working day in Hangzhou was selected as the research object of this study. The results show that (1) morning peak commuting carbon emissions in the main urban area of Hangzhou reached 2065.14 kg per hour, accounting for 13.73% of all taxi travel carbon emissions; and evening peak commuting carbon emissions reached 732.2 kg per hour, accounting for 4% of all taxi travel carbon emissions; (2) At the grid level, the spatial distribution of commuting carbon emissions in Hangzhou shows a single central peak that decays in all directions; and (3) The results at the resident community scale show that urban public transport facilities influence resident community commuting carbon emissions. In areas such as at the urban-rural border, resident community commuting carbon emissions show high levels of aggregation, and in the main urban area, resident community commuting carbon emissions show low levels of aggregation. This study not only provides a new method of commuting investigation but also offers constructive suggestions for future carbon emission reduction under Hangzhou’s urban planning.