Abstract

Low-carbon (LC) cities are the cities that people long for today. LC environmental protection plays a very important role in people’s health. The construction of a city in LC is a great cause that contributes to the present and benefits the future. In this study, we propose a development and sustainability evaluation system for building low-carbon cities based on the Internet of Things (IoT). The study is novel in that it considers key areas such as urban planning, environmental issues and solutions, and how the Internet of Things can optimize low-carbon logistics and smart grids, with the aim of promoting the formation of low-carbon city models. This comprehensive approach not only presents problems and solutions in low-carbon urban planning but also focuses on how the Internet of Things can be used as a key technology to promote low-carbon urban development. The urban development of LC was constructed with a sustainable evaluation system, so that people could experience the life of LC. Through the investigation of the degree of atmospheric pollution of LC cities using the Internet of Things, this article found that the highest degree of atmospheric pollution was 30. The highest degree of atmospheric pollution in cities in LC without IoT was 53. The severity of water pollution in cities in LC using IoT technology ranged from 10 to 25, while those without IoT ranged from 30 to 60. The degree of soil pollution in LC cities using IoT technology was concentrated in 10–30, while those without using IoT were concentrated in 30–50. Through these experimental data, it could be seen that IoT technology could reduce environmental pollution, thus achieving the effect of LC cities. This shows that the use of IoT technology in LC cities was highly feasible.

Introduction

LC cities are currently a prominent topic of discussion and delight the imaginations of people around the world. The concept of LC cities, short for “Low-Carbon” cities, has gradually gained traction among the populace. People harbor a deep yearning for the LC lifestyle, envisioning medium-sized cities that are environmentally friendly and pristine, capable of fostering both health and happiness. Consequently, there is an ongoing surge in scholarly endeavors aimed at comprehensively understanding LC cities.

As public interest in LC cities continues to increase, so does the volume of research dedicated to unraveling their intricacies. Liu C, for instance, espoused the view that LC policies are not merely geared towards fostering economic development, but also towards mitigating the impacts of climate change. He posited that LC policies foster a symbiotic relationship between economic industrial structures and foreign investments, thus achieving mutually beneficial growth. Despite the promotion of regional innovation by LC policies, Liu cautioned that innovation might not produce immediate results [1]. Similarly, Xin L stated that the promotion of green and LC development has become a global consensus, but lamented the dearth of studies exploring the scientific nuances and repercussions of LC development [2]. Chen L, on the other hand, emphasized that the primary environmental challenge lies in carbon emissions arising from urbanization. He advocated for stringent measures to curb the growth of urban carbon emissions as part of experimental policies aimed at achieving carbon neutrality [3]. Colenbrander S shed light on the prevalent misconception that LC development is inherently more expensive than high-carbon development, a belief that often hampers opportunities for sustainable growth, especially in resource-constrained environments [4]. It is posited that integrating LC city construction with the Internet of Things (IoT) could potentially catalyze more comprehensive LC development, thereby fostering cities’ transformation towards greater sustainability.

Conclusions

With the increasing severity of pollution, people gradually began to pay attention to the development of LC, which can reduce environmental pollution and bring health to people. As a new technology, the IoT can play a very good role in the construction of LC cities. Cities become intelligent and can reduce many unnecessary pollutants, thus achieving the effect of reducing pollution. This paper studied the development of LC cities based on IoT, in hopes of improving the construction of LC cities through IoT technology. This paper tested the air pollution level, water pollution level, and soil pollution level of LC cities using the IoT technology and without the IoT technology. From the experimental data, it could be seen that the level of air pollution level, water pollution level, and soil pollution level after using IoT technology were lower than those without using IoT technology, which proved that the IoT technology was suitable for the LC development of cities and could reduce pollution. Due to the length of this paper, the experiments and discussions on LC cities were still insufficient and would be improved in the future. Finally, it was hoped that the LC of the city could develop better and better and that people could lead a life that was environmentally friendly and LC.

Abstract

Introduction

Material and methods

Results and discussion

Conclusions

Acknowledgments

Supplementary materials

Reference

Abstract

     Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep learning model, iDeepAir, to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality. Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355 μg/m3 to 12.283 μg/m3 compared with other models. And identifies the ranking of major factors, local meteorological conditions have become a nonnegligible factor. Layer-wise relevance propagation (LRP) is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM2.5 concentration in various regions of Shanghai. Meanwhile, As the strict and effective industrial emission reduction measurements implementing in China, the contribution of urban traffic to PM2.5 formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03% in 2011 to 24.37% in 2017 in Shanghai, and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction.

Introduction

     Air pollution is a large obstacle to the world’s future sustainable developments, and millions of people die from air pollution-related diseases every year around the world (Zheng et al., 2017). This is seriously severe in some developing countries like China (Lelieveld et al., 2015), which has the highest country-level values globally for the population-weighted annual average concentration of PM2.5 (Tichenor and Sridhar, 2019; Zhang et al., 2012) and has been a major public health concern in recent years (Li et al., 2019a) . Shanghai, one of the most developed and populous cities in China, has suffered severe increasing haze episodes mostly attributed to the severe particle pollution especially fine particles (particles ≤2.5μm in aerodynamic diameter; PM2.5)(Han et al., 2020) since 1990s with the rapid urbanization and industrialization (Wang et al., 2015) . Under these circumstances, air pollution-related diseases have emerged gradually, such as respiratory diseases in the elderly and preterm birth and low birth weight for birth when maternal exposure to PM2.5 in Shanghai(Li et al., 2019a; Liu et al., 2017) .

Conclusions

     In this article, we propose a novel method to quantify the influence of anthropogenic emissions on PM2.5 concentration with a novel and traceable deep learning model. Our experiment results indicate that the proposed model could achieve better fitting and prediction performances than the LSTM, GBRT, Seq2Seq, DA-RNN model and other deep learning models. Furthermore, we output the contributions of input parameter to the prediction in LRP method and visualize and analyze the spatial correlation between traffic flow and PM2.5.

     In addition, we discover that transportation emissions will play the most dominate role in future urban air pollution, and how to reduce traffic emissions are an unavoidable issue on achieving sustainable developments in modern cities. Meanwhile, to some extent, new energy vehicles can be considered as an effective way to reduce traffic emissions. However, the current promotion policies and efforts are far from enough. To further improve urban air quality, we need some more effective and powerful measurements in response to the rapid growth of urban traffic emissions.

Abstract

1. Introduction

2. Literature review

3. Integrated analytical framework for sustainability analysis of supply chain management

4. Results

5. Conclusions

Disclosure statement

References

Abstract

     With the increasing importance of air pollutant emissions to the platform economy and green supply chain management, it is essential to analyse the trend and correlation between particulate matter emissions and supply chain statistics. Typical approaches do not integrate particulate matter prediction with the sustainability analysis, and suffer from common issues such as low classification accuracy and unstable prediction performance. In this study, we propose an integrated analytical framework for sustainability analysis of supply chain management through particulate matter emissions prediction. Specifically, we performance trend and correlation analysis between particulate matter emissions (PM2.5 and PM10) and supply chain statistics in Beijing of China. We combine the boosting algorithm and neural network method to predict particulate matter emissions. Experimental results show that our prediction model achieved high performance. Sustainability analysis shows that the steady growth of the supply chain operations is accompanied by decreasing air pollutant emissions in China.

Introduction

     Today, human society is facing very severe environmental and resource problems. The green and sustainable supply chain are the concrete manifestations of sustainable development strategies that comprehensively consider these two issues in platform economy management. In recent years, continued urbanisation and industrialisation, particularly in developing countries, has led to severe deterioration in air quality and a rapid increase in the degree of contamination (Shi and Yu 2020). Air pollution has substantially affected the living environment of the human population and endangered health (Stieb et al. 2019). The United Nations (UN) issued Sustainable Development Goals (SDGs) in 2015, serving as a blueprint for building a more sustainable future across the globe. There are totally 17 goals in SDGs, addressing issues in economic, environmental, and social areas. These goals are interrelated and expected to be fulfilled by 2030 (2015). PM2.5 emissions have an adverse effect on public health (Yin, Pizzol, and Xu 2017). At present, many countries attach great importance to inhalable total suspended particulates that cannot be blocked by the human upper respiratory tract, especially inhalable aerosol particles with sizes less than 2.5 μm. It is necessary to arouse the stakeholder

Conclusions

     In this paper, an integrated analytical framework targeting sustainability analysis of supply chain management through particulate matter emissions prediction has been proposed. Specifically, we firstly collect and preprocess the particulate matter emission data and supply chain statistics in Beijing of China, then we conduct correlation analysis between air pollutant concentrations and supply chain statistics to uncover the intrinsic associations. Secondly, we combine the boosting ensemble meta-algorithm and neural network method for particulate matter concentration forecasting. The boosting algorithm is used to train a set of individual subnets to improve the efficiency of prediction, and afterwards, the output results of individual subnets are weighted for acquiring the final prediction results. Finally, we perform an analysis on sustainability between air pollutant emissions and supply chain statistics after obtaining the particulate matter prediction results.

     This analytical framework is able to perform sustainability analysis with accuracy and provide reliable insights for the legislation and implementation of green supply chain management. The forecasting of particulate matter emissions conveys basis and justifications for policy-makers to enforce the environmental protection measures associated with air pollution and design efficient policies with the aim of improving the air pollutant management of the supply chain, which will further lead to an improved public health environment, especially in economically developed areas. Because this study mainly targets the prediction of PM2.5 and PM10 concentrations, it cannot comprehensively reveal the geographical distribution of air pollution. There are various factors that affect air quality, and a regression model can be established to predict PM2.5 and PM10 distributions more comprehensively and to determine the key influential factors, which would be important for formulating economic development strategies and implementing environmental protection mechanisms.

Abstract

1. Introduction

2. Methods and Materials

3. International Perspective on Air Pollution

4. General and Respiratory Disease Hospitalization and Mortality Rates

5. Children

6. Adults

7. COVID-19 Pandemic

8. Social Awareness of Air Pollution in Poland

9. Limitations and Future Perspectives

10. Conclusions

Abbreviations

References

Abstract

     According to the World Bank Group, 36 of the 50 most polluted cities in the European Union are in Poland. Thus, ambient air pollution and its detrimental health effects are a matter of immense importance in Poland. This narrative review aims to analyse current findings on air pollution and health in Poland, with a focus on respiratory diseases, including COVID-19, as well as the Poles’ awareness of air pollution. PubMed, Scopus and Google Scholar databases were searched. In total, results from 71 research papers were summarized qualitatively. In Poland, increased air pollution levels are linked to increased general and respiratory disease mortality rates, higher prevalence of respiratory diseases, including asthma, lung cancer and COVID-19 infections, reduced forced expiratory volume in one second (FEV1) and forced vital capacity (FVC). The proximity of high traffic areas exacerbates respiratory health problems. People living in more polluted regions (south of Poland) and in the winter season have a higher level of air pollution awareness. There is an urgent need to reduce air pollution levels and increase public awareness of this threat. A larger number of multi-city studies are needed in Poland to consistently track the burden of diseases attributable to air pollution.

Introduction

     Ambient air pollution is one of the most significant environmental issues affecting health and wellbeing. It is linked to increased mortality from cardiovascular and respiratory diseases, including lung cancer and chronic obstructive pulmonary disease (COPD), shortening life expectancy [1]. It is estimated that in Europe in 2019, about 307,000 premature deaths were caused by chronic exposure to fine particulate matter with a diameter of 10 micrometres or smaller (PM10) and to coarse particulate matter with a diameter of 2.5 micrometres or smaller (PM2.5) [2]. In addition, about 40,000 premature deaths were attributed to chronic nitrogen dioxide (NO2) exposure [2].

     According to the World Bank Group, 36 of the 50 most polluted cities in the European Union are in Poland [3]. Moreover, Polish cities are among the urban areas with the highest estimated PM mortality burden [4]. Despite a consistent decrease in annual outdoor PM concentrations in the most polluted Polish cities in the recent decade, current values still do not meet the World Health Organisation (WHO) guidelines [5,6].

Conclusions

     In Poland, increased air pollution levels are linked to increased general and respiratory disease mortality and hospitalization rates as well as a higher prevalence of respiratory symptoms (wheezing, dyspnoea attacks, chronic cough) and respiratory diseases, including bronchitis, asthma, rhinitis, lung cancer and COVID-19 infections. Reduced FEV1 and FVC in spirometry tests are also observed. Infants, children, adolescents as well as adults are affected by worse air quality. Living in the proximity of high traffic areas exacerbates respiratory health problems.

     There is ample evidence that the course of asthma and bronchitis is exacerbated in areas with high ambient air pollution, while the indicators of subjective quality of life in asthmatic patients are lower. In atopic subjects, air pollution seems to sensitise the body and trigger an excessive response when combined with the usual allergen. There is also some evidence that higher concentrations of PM2.5, PM10, SO2, NO2 and ozone may facilitate the spread and infectiousness of SARS-CoV-2.

Abstract

     While electrification of road transport is a key component of decarbonisation, the implications for the broader economy and related jobs remain underexplored. We quantify these impacts in the EU in a global Computable General Equilibrium (CGE) model, combining techno-economic assumptions about electric vehicles with deployment scenarios derived by energy models. We augment input-output tables underlying the JRC-GEM-E3 model with an explicit representation of vehicle manufacturing and upgrade the modelling of vehicle purchase and operation. Our findings illustrate that greater road transport electrification reduces the overall costs of climate mitigation, primarily driven by lower fuel costs for electric vehicles and a faster decline of battery costs. Transport electrification alters supply-chains and leads to structural shifts in employment from traditional vehicle manufacturing towards battery production, electricity supply and related investments. Finally, we expand the set of labour market indicators to cover skills and occupations, to refine the socio-economic assessments of climate policy.

Introduction

     Global CO2 emissions from transport have continued to rise in recent years across all modes (IEA, 2020a). In the EU, transport greenhouse gas (GHG) emissions were 33 % higher in 2019 compared to 1990 levels, while other sectors achieved emissions reductions over the same period (e.g. energy supply −39 %, industry −35 %, EEA, 2020). This is in stark contrast with the EU’s objectives of 55 % emission reductions by 2030 compared to 1990 and to achieve net zero emissions by 2050.1

     Road transport, accounting for nearly one-fifth of total EU GHG emissions, has therefore become a priority sector for climate action and a key element of the European Green Deal (European Commission, 2019). To encourage rapid emissions reductions in road transport, Europe has recently proposed new initiatives for stronger emissions standards for cars and vans, the set-up of a new emission trading scheme for buildings and road transport, as well as new regulation for alternative fuel and charging infrastructure (European Commission, 2021, European Commission, 2021).

Conclusions and policy implications

     The electrification of road transport is accelerating across the globe, with significant increases in electric vehicle market shares in China, Europe and the United States in particular (IEA, 2020c). While long-term projections on the number of EVs in the car and truck fleets vary across models and methodologies, the technology is widely expected to make up the majority of the total vehicle stock by mid-century, as a result of increasingly stringent climate policies and technology advancements, notably in batteries. Such fleet transformation in the coming three decades will be associated with large shifts in the manufacturing of motor vehicles and their supply-chain in the near-term, as well as changes in the operation of vehicles (fuel shifting, energy efficiency, maintenance schedules, etc.).

     This paper explores the macroeconomic implications of road transport electrification as a contributing policy to reaching Europe’s climate targets. We combine techno-economic assumptions about the composition and operation of the fleet with fuel consumption projections from energy system models into a set of climate mitigation scenarios with varying degrees of road transport electrification. We run these scenarios in an augmented version of the JRC-GEM-E3 model, which explicitly represents the manufacturing of BEVs as a separate economic activity from the manufacturing of conventional ICE vehicles. This new methodology allows for a more detailed analysis of macroeconomic impacts in two major ways: first the supply chain implications of the deployment of EVs can be more clearly identified than if all vehicles were produced using a single input structure. This has large implications in terms of employment, and therefore on occupations and skills. Second, this also allows for a new decomposition analysis, where the various channels of impacts from EVs on macroeconomic results can be explored. Our modelling suggests that a higher degree of road transport electrification can contribute to reducing the costs of climate mitigation policies.

Abstract

Introduction

Materials and methods

Phase i: data gathering and air quality index (AQI)

Phase ii: geostatistics and spatial analysis of AQI data

Spatial autocorrelation (Moran’s index)

Results

Discussion

Conclusion

Declarations

References

Abstract

     In the global COVID-19 epidemic, humans are faced with a new challenge. The concept of quarantine as a preventive measure has changed human activities in all aspects of life. This challenge has led to changes in the environment as well. The air quality index is one of the immediate concrete parameters. In this study, the actual potential of quarantine effects on the air quality index and related variables in Tehran, the capital of Iran, is assessed, where, first, the data on the pollutant reference concentration for all measuring stations in Tehran, from February 19 to April 19, from 2017 to 2020, are monitored and evaluated. This study investigated the hourly concentrations of six particulate matters (PM), including PM2.5, PM10, and air contaminants such as nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO). Changes in pollution rate during the study period can be due to reduced urban traffic, small industrial activities, and dust mites of urban and industrial origins. Although pollution has declined in most regions during the COVID-19 quarantine period, the PM2.5 rate has not decreased significantly, which might be of natural origins such as dust. Next, the air quality index for the stations is calculated, and then, the interpolation is made by evaluating the root mean square (RMS) of different models. The local and global Moran index indicates that the changes and the air quality index in the study area are clustered and have a high spatial autocorrelation. The results indicate that although the bad air quality is reduced due to quarantine, major changes are needed in urban management to provide favorable conditions. Contaminants can play a role in transmitting COVID-19 as a carrier of the virus. It is suggested that due to the rise in COVID-19 and temperature in Iran, in future studies, the effect of increased temperature on COVID-19 can be assessed.

Introduction

     In late December 2019, a new strain of an infectious disease was discovered in Wuhan, China, and was named COVID-19 (Chen et al. 2020a, b, c; Zhang et al. 2021; Afify et al. 2021). This virus, which is a human-to-human transmitted virus (Wang et al. 2020; Wang and Su 2020), causes an acute respiratory infection that can progress to pneumonia if the symptoms are not treated (Jiang et al. 2020a, b; Coccia 2021). The findings indicate that old age is one of its risk factors (Luo et al. 2020; Wang and Zhao 2021) with an approximate 2–3% fatality rate (total infected people who died) (Rodriguez-Morales et al. 2020; Bonilla-Aldana et al. 2020). On February 19, 2020, the Iranian Ministry of Health officially reported that two patients with pneumonia were associated with COVID-19 in the city of Qom. Gilan, Arak, and Tehran were the other provinces where this virus spread widely. Thereafter, the count of infected populations increased rapidly, and within 1 month, the outbreak became a national crisis, with infected individuals diagnosed all over the country (Iran Health Organization 2020). To control this infection, the National Corona Committee was deployed with subcommittees in each province. To prevent the spread of the disease, this committee has enacted measures to ban traffic at different times of the day and close or reduce the operation time of high-risk industries, factories, etc. These measures were lifted from 13 to 20 April. As of 4 May, the confirmed cases are 97,424, and the deaths are 6203 (World Health Organization 2020).

Conclusion

     COVID-19 is a human hazard that has had different effects at different times and different places. One of the effects of COVID-19 is its effect on traffic, and its secondary effect is on air quality. This paper examines changes in air quality due to the quarantine caused by COVID-19. This study investigated the hourly concentrations of six particulate matters. Findings indicated that changes in pollution rate during the study period can be due to reduced urban traffic, small industrial activities, and dust mites of urban and industrial origins. Although pollution has declined in most regions during the COVID-19 quarantine period, the PM2.5 rate has not decreased significantly, which might be of natural origins such as dust. The results showed that the increasing percentage of the count of days caused by PM2.5 pollution has a sharp increase, which was at the beginning of the curfew on March 2. The AQI slope showed a further decrease compared to previous years. This rate followed a descending pattern until March 27, and the maximum intensity of the fixed trend index increased until April 12. The results of this study indicate that the quarantine has not been able to significantly affect the mean air quality index; this is because of the use of private cars instead of public transport, which is more evident in areas 6 and 7. On the other hand, the maximum air quality index expressed in Fig. 10 has decreased. Spatial autocorrelation analysis has shown a positive correlation between changes in decrease or increase in indicators. Finally, it can be said that although COVID-19 can affect the rate of contamination in a short period of time, due to the prevalence of COVID-19 in crowded places such as public transport fleets, people have been forced to use personal items, and as a result, pollution has increased.

1. Introduction

2. Materials and methods

3. Results

4. Discussion

5. Conclusions

Acknowledgments

Appendix A. Supplementary data

References

Abstract

The military sector is an important global player in terms of monetary expenditure and resource use. However, reporting of military greenhouse gas emissions is often embedded into other activities and quantitative estimations are scarce. This paper assesses the life cycle greenhouse gas emissions from the Norwegian defence sector from an organisational perspective. The total annual emissions add up to 0.8 million tonnes of CO2 equivalents, corresponding to approximately 1.1% of the national emissions from Norwegian consumption. The results show that upstream activities are the main contributors to emission (68%), with only 32% allocated to the reporting organisation. From a management perspective, this distinction is important since these emissions may be mitigated through green procurement practices, in contrast to direct emissions that require operational reductions.

Introduction

World military expenditure is estimated to have reached $1739 billion, representing 2.2 percent of the global gross domestic product (GDP), in 2017 (Tian et al., 2018). The military sector and defence industry are therefore major global players, using considerable resources and subsequently affecting the environment. Even though the environmental impact of military activities has been discussed and debated for centuries, few documented studies of the sector’s environmental impact exist and most are connected to biodiversity and land use (Hanson, 2018; Lawrence et al., 2015; Nuttall et al., 2017; Vertegaal, 1989; Zentelis et al., 2017). Indirect correlations between military energy use, especially fossil fuel use, and greenhouse gas (GHG) emissions have been discussed previously (Bildirici, 2017; Clark et al., 2010; Nuttall et al., 2017), but quantitative estimations are scarce. According to the Kyoto protocol and, subsequently, the Paris agreement, emissions from military activities are to be included in the national emissions inventory if they are accrued within national borders. Reporting of overseas activities or impacts of warfare is not required. The emissions from military activities are often embedded into other activities, such as energy production, transportation, and industrial activities, or taken out of the reporting (Michaelowa and Koch, 2001). A few studies on sector-specific calculations of greenhouse gas emissions in the UK and Australia have been found, indicating that defence activities contribute to approximately 1% of the annual emissions of greenhouse gases in these countries (Bailey, 2009; Wood and Dey, 2009). Figures from the US are within the same range, varying from 25.4 million tonnes annually from direct fuel consumption (Belcher et al., 2019) to 172 million tonnes including electricity use and upstream emissions (Liska and Perrin, 2010). This is equivalent to 0.5e3.3% of the total US emissions in 2017 (EIA, 2019).

1- Introduction

2- Problem statement

3- Methodology

4- Case studies

5- Conclusions

References

Abstract

Flare is the last safety measure for daily operations in oil, gas & chemical process industries (OGCPI). However, an excessive flaring releases large quantity of emissions of VOCs and NOx, which may suddenly enhance local ozone as a secondary pollution. Normally, the flare destruction and removal efficiency (DRE) of 98% or 99% is regulated as the national standard and presumed for industrial practices in the U.S. Unfortunately, real DRE values could be much lower than the standard due to impact factors including various meteorological and operating conditions such as the cross-wind speed, flare jet velocity and heating value of combustion. Thus, it is critically important to explore the sensitivity of the regional ozone impact due to low DREs of OGCPI flare combustions. In this paper, a systematic methodology has been developed to examine ozone impacts due to the low flare DREs, which have never been systematically studied before. The DRE formulas were derived from computational fluid dynamic (CFD) modeling and Water Environment Research Foundation (WERF) results and then employed to recompile the point source emission inventory. After that, comprehensive air quality model with extensions (CAMx) was employede to simulate and quantify local ozone changes impacted by flare emissions of OGCPI. Case studies indicate that the maximum hourly ozone increments due to the low DRE through CFD and WERF modeling is 0.18 ppb and 1.3 ppb, respectively. This study could enrich fundamental understandings of industrial point source emissions and provide the quantitative and valuable support for the ozone pollution caused by OGCPI flare emissions under low DRE instead of standard values.

Introduction

Industrial flaring is to safely combust off-spec, unusable, or unwanted process streams, which might otherwise be harmful to local environment if directly vented without destructions. The oil, gas and chemical process industries (OGCPI) in the U.S. daily processes millions of cubic feet of hydrocarbon gases (Baukal and Schwartz, 2001; Aalsalem et al., 2018). Thus, a slight decrease in flaring performance will release millions of cubic feet of gaseous emissions into the atmospheric environment. Note that although flaring is a safety measure for plant safety in OGCPI, excessive flaring will generate large amounts of emissions such as NOx (nitrogen oxides), CO2, CO, VOCs (volatile organic compounds) especially for high reactive VOCs (i.e., HRVOCs such as ethylene, propylene, acetylene). For instance, an olefin plant with a capacity of 1.2 billion pounds of ethylene productivity per year can easily flare about 5.0 million pounds of ethylene during one single start-up 50 operation (Xu and Li, 2008).

1- Introduction

2- Background and related literature

3- Research design

4- Empirical results

5- Further analysis

6- Conclusion

References

Abstract

This study investigates the causal impact of market-based environmental regulation on firm innovation by examining a large-scale market-based regulatory attempt in a developing country, namely, China’s sulfur dioxide (SO2) emissions trading program. Based on the panel data of China’s publicly traded firms from 2004 to 2015, we adopt the difference-in-differences (DID) model to examine the innovation effects of the SO2 emissions trading pilot policy. The results show that the program leads to a significant increase in patents and environmental patents among regulated firms. And the innovation effects of the policy perform better in areas with a high level of environmental enforcement. In further analysis, we find that the program decreases SO2 emissions and promotes industrial growth in pilot areas. These evidences imply that the market-based emissions trading policy indeed promotes firm innovation and environmental innovation even in the context of a developing country, which is conductive to a win-win situation in both environmental protection and economic growth.

Introduction

Air pollution and climate change are among the most pressing current global environment challenges. To address these challenges, emissions trading schemes (ETSs) have assumed an ever more prominent role among various environmental policies over past decades, and many countries have begun implementing ETSs. The early programs include such as the American Acid Rain Program and the European Union (EU) carbon emissions trading system etc. Until recently, Australia, New Zealand and Canada have also launched the programs to regulate greenhouse gas emissions. Other significant economic areas (e.g., Brazil and Mexico) are in the process of initiating ETSs. With so many programs in the works, ETSs are environmental policies with the largest economic scope in the world today (Calel and Dechezleprêtre, 2016; Taylor, 2012). In the most basic form of the ETS, policy makers set a cap on the quantity of permissible emissions and distribute allowances to emitters that collectively sum to the cap. Firms can freely trade the allowances on the market but must surrender the number of allowances that are equivalent to the amount of emissions at the end of each year (Rogge et al., 2011; Taylor, 2012). The primary goal of the ETS is to achieve a given reduction target at minimal cost; however, it is also critical for such policy providing incentives for technological innovation. Because technological innovation is not only an important driving force for addressing long-term environmental problems and achieving sustainable environment (Inoue et al., 2013) but also an important factor on firms’ productivity growth and competitiveness (Aghion et al., 2016). Therefore, the focus of this study is to examine whether a market-based ETS can influence firm innovation, which is a key determinant of a win–win solution for both environmental quality and economic growth. In theory, an emissions trading program can create two sets of countervailing incentives: on one hand, the program allows firms to meet pollution reduction obligations by purchasing allowances from other emitters, which may reduce their incentive to innovate in the presence of uncertainty during the innovation process (Rogge et al., 2011; Taylor, 2012). On the other hand, the Porter Hypothesis postulates that strict but flexible environmental regulations, such as the ETS, may provide incentives for technological change (Cohen and Tubb, 2018; Jaffe et al., 2002). The ETS may provide incentives for sellers of allowances to innovate to reduce emissions and enable them to sell more allowances. The conflicting incentives imply that the overall impact of the ETS on firm innovation remains unclear. On the empirical evidence side, abundant literature has investigated the innovation effects of such policy.

1. Introduction

2. Method and data

3. Results and discussion

4. Conclusions

Acknowledge

Appendix A. Supplementary data

References

Abstract

The petroleum refining industry is an important contributor to industrial volatile organic compounds (VOCs) emission. As a foundational work for VOCs control, previous VOCs inventories usually treated the petroleum refining industry as an integrated part resulting the unknown of VOCs emission characteristic among different units. Refined management has becoming an inevitability with the increasing strictness of environment standards. This study aims to develop a holistic method for VOCs emission inventory of the petroleum refining industry toward specificity, accuracy and economization, in which a source categorization was proposed from unit angle, and a systematic estimation method was developed from the material flow point of view. This method provides a more specific and accurate quantification method, especially for fugitive emission sources which is a pivotal but difficult problem in VOCs inventory establishment. Through application to a typical medium-scale refinery located in northern China, a unit-specific VOCs emission inventory with 48 emission sources and respective local VOCs emission factors (EFs) was established. Estimation results and economic cost of the developed method were compared with those of other methods. In this study, the integrated EF was 0.77 kg-VOCs/t-crude oil refined, which was in the same order with most previous studies. Inventory results implied that increasing hydrotreating unit, reducing chemical device and solvent-used unit (e.g., polypropylene production, furfural refining unit), upgrading catalytic reforming unit were beneficial to control VOCs emissions in this study. Using floating-roof tanks rather than fixed-roof tanks is an effective way to reduce VOCs emissions from storage tanks, which decrease 72%–86% of diesel storage emissions. Economic cost analysis showed that the advantage of this method lied in lower labor cost, and no subsequent monitoring cost. Suggestions proposed from this study provide feasible measures for local policy makers to control VOCs emission and determine abatement strategies of the petroleum chemical industry. Meanwhile, this study greatly helps enterprises promote the fine management of VOCs-containing materials from the overall processes to identify VOCs control emphasis.

Introduction

Air pollution characterized by high concentration of groundlevel ozone (O3) and fine particulate matter (PM) has caused increasing concerns in China due to its significant adverse impact on human health (Dai et al., 2017; Rohde and Muller, 2015). Volatile organic compounds (VOCs) are key precursors for the formation of ozone and PM through complicated photochemical and physical reactions (Shao et al., 2009a, 2009b; Yuan et al., 2013). Ozone reduction is largely accomplished by the control of VOCs (Tang et al., 2012). Chinese government has been focusing on VOCs pollution problem. The 13th five-year plan for ecological and environmental protection made the reduction target that the total national VOCs should decreased by more than 10% during the 13th five year. Standard for fugitive emission of volatile organic compounds (MEEC, 2019b) and comprehensive VOCs treatment scheme for key industries (MEEC, 2019a) were successively published to promote VOCs prevention work, recently. The petroleum refining industry is an important contributor to VOCs emissions, accounting for approximately 3% of the total anthropogenic VOCs emissions (Wei * Corresponding author. et al., 2016). In 2015, the petrochemical industry become one of the first batch of pilot industries to implement VOCs Pollution Pilot Charge Method published in China. Thus, a specific VOCs emission inventory of the petroleum refining industry is necessary and foundational for VOCs emissions reduction and equitable control policies.

Air pollution over China has attracted wide interest from public and academic community. PM2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM2.5 concentrations are essential to understand the variability of PM2.5 and seek methods to control PM2.5. Since 2013, the measurement of PM2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM2.5 concentrations. We start with an introduction of general meteorological conditions and PM2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM2.5 concentrations. Next, major methods used to quantify meteorological influences on PM2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM2.5. The feedback effects of PM2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM2.5 pollution are made finally.

Introduction

PM2.5(atmospheric particles with aerodynamic diameters less than or equal to 2.5 μm) has become one major airborne pollutant across China and attracted widespread public interest, when a severe and persistent pollution episode occurred in Beijing in December 2012. Since then, PM2.5 dominated haze episodes have been frequently reported across China (Huang et al., 2014; He et al., 2017; etc.). Due to its negative influence on human health (Lanzinger et al., 2016; Li et al., 2015b; etc.), studies concerning the characteristics (Zhang et al., 2013; Wang et al., 2016; etc.) and sources (Gu et al., 2014; Liu et al., 2014; etc.) of PM2.5 have been massively conducted in recent years. One key step for comprehensively estimating and managing PM2.5 pollution is to quantify its major influencing factors. Anthropogenic emissions have been widely accepted as the dominant driver for PM2.5 concentrations, whilst meteorological conditions also exert a strong influence on long-term PM2.5 variations (He et al., 2017). Yang et al. (2016) quantified that meteorological conditions alone contributed 1.8 out of 15 µg m−3 decade−1 PM2.5 variation in eastern China from 1985 to 2005. Gui et al., (2019) calculated that meteorological conditions accounted for 48% of PM2.5 variations in Eastern China from 1998 to 2016. Zhang et al. (2018) indicated that the relative contribution of meteorological conditions to heavy pollution episodes within BeijingTianjin-Hebei region was larger than 50% from 2013 to 2017. Chen et al., (2019) indicated that meteorological variations contributed about 20% of PM2.5 reduction in Beijing from 2013 to 2017. To further examine the strong, yet inconsistent meteorological influences on PM2.5 concentrations, many studies have been conducted and suggest that multiple meteorological factors, including temperature (Li et al., 2015a; You et al., 2017; etc.), wind(Yin et al., 2017; etc.), humidity (Liao et al., 2017; Cheng et al., 2017; etc.), precipitation (Li et al., 2015; Guo et al., 2016; etc.), radiation (Chen et al., 2017), atmospheric pressure (Zhang et al., 2015; You et al., 2017; etc.) and planetary boundary layer height (Du et al., 2013; Zheng et al., 2017; etc.), are closely related to PM2.5 concentrations.

1. Introduction

2. Evaluation framework

3. Evaluation results and discussion

4. Conclusions

References

Abstract

China has emerged as a world leader in the coal chemical industry, which requires large amount of water and results in considerable CO2 emissions. This situation has led to the challenge of the CO2-Water nexus for China and particularly for the sustainable development of its coal chemical industry. CO2-enhanced water recovery (CO2-EWR) technology can provide large-scale CO2 mitigation and additional water supply in an integrated manner, especially in arid areas. Meanwhile, CO2 streams from industrial separation processes in the coal chemical industries are amenable to separation and can dramatically simplify or even dispense with the capture process. This study presents the first systematic assessment of a cost curve for onshore CO2-EWR potential using CO2 streams from industrial separation processes by an evaluation framework encompassing CO2 emission inventory, site suitability evaluation, and source–sink matching with techno-economic models. Preliminary results focused on the full capacity of several coal chemical processes as of 2015 suggest that CO2-EWR technology can mitigate 269 million tons of CO2 from industrial separation processes at relatively low cost ranging from 12 to 30 USD/t CO2 in China. Furthermore, 404 million tons of underground water could be produced for further desalination and utilization. When additional capacity under development could become fully operational, the emissions of 878 million tons of CO2 could be mitigated and provide 1318 million tons of vital water resources. Therefore, CO2-EWR technology can be essential to clean and sustainable development of the coal chemical industry and may provide low-cost opportunities to accelerate the deployment of large-scale CCUS projects in China.

Introduction

China’s abundant coal reserves and urgent concerns about energy security and economic development have driven local governments in coal-rich regions to invest in coal chemical technology. China has emerged as a world leader in the coal chemical and coal conversion industry. However, the environmental impact resulting in huge CO2 emissions and water consumption caused by this dramatic development of the coal chemical industry are concerns for stakeholders, such as governments, investors, enterprises, and the public in China. Carbon capture, utilization and storage (CCUS) technology is an essential component to reduce CO2 emissions and produce value-added products on a meaningful scale. Among these options of CCUS technologies, CO2 capture and CO2 aquifer storage with CO2-enhanced water recovery (CO2-storage/CO2-EWR, abbreviated as CO2-EWR) are considered an effective approach to large-scale CO2 mitigation and water production with relatively high technology readiness levels and low cost, especially in arid regions with high water stress and high water price (Davidson et al., 2009; Davies et al., 2013; Kobos et al., 2011; Ziemkiewicz et al., 2015).

China’s abundant coal reserves and urgent concerns about energy security and economic development have driven local governments in coal-rich regions to invest in coal chemical technology. China has emerged as a world leader in the coal chemical and coal conversion industry. However, the environmental impact resulting in huge CO2 emissions and water consumption caused by this dramatic development of the coal chemical industry are concerns for stakeholders, such as governments, investors, enterprises, and the public in China. Carbon capture, utilization and storage (CCUS) technology is an essential component to reduce CO2 emissions and produce value-added products on a meaningful scale. Among these options of CCUS technologies, CO2 capture and CO2 aquifer storage with CO2-enhanced water recovery (CO2-storage/CO2-EWR, abbreviated as CO2-EWR) are considered an effective approach to large-scale CO2 mitigation and water production with relatively high technology readiness levels and low cost, especially in arid regions with high water stress and high water price (Davidson et al., 2009; Davies et al., 2013; Kobos et al., 2011; Ziemkiewicz et al., 2015). pressure) (Feng et al., 2013; Meng et al., 2007; Zhao and Gallagher, 2007). This results in large quantities of high purity CO2 streams available from the coal chemical industry, and when combined with CO2-EWR offers an opportunity to address the energy-water-climate and economic challenges facing the coal chemical industry, large-scale deployment of CCUS technologies, and China’s low-carbon future (ADB, 2015; Zhang et al., 2013).

Exposure to phthalate esters (PAEs) poses health risks to humans. Much research has been performed evaluating PAE levels in foodstuffs, river sediment and drinking water, but little attention has been paid to their presence in urban outdoor environments where human activities are highly intense. Here we evaluated PAE presence and distribution in street dust in Kaohsiung, the most industrialized city in Taiwan. Our results showed that PAEs were ubiquitous in fifty-two street-dust samples (levels of total PAEs 5.4–989.2 mg kg−1 ). Di-(2-ethylhexyl) phthalate was the most abundant congener observed and made up 85.0%, 79.7%, and 97.2% of the total PAEs found in industrial, residential and commercial areas, respectively. PAE levels in street dust in commercial areas (night markets) were significantly higher, suggesting a higher risk of contamination on people present in these areas (H value N χU 2). In residential and commercial areas, the higher the intensity of human activity, the higher the PAE content observed. PAE content decreased progressively from the center to the outskirts of the Houjing night market, suggesting that the increased human and consumer activities inside this commercial hotspot were the main PAE source in street dust. Children had higher estimated daily intakes (DIs) than adults and dermal absorption contributed more to these levels than oral ingestion. Although all calculated DIs were below referenced danger thresholds, street dust PAEs in the area should remain an environmental concern especially since night markets play an important role in Taiwanese/Asian culture and economy. Contrary to other studies, PAEs in this study were found less related to industrial manufacturing activities but highly linked to commercial activities. These findings are relevant for future pollution prevention efforts dedicated to mitigating public exposure to PAEs.

Introduction

Phthalate esters (PAEs), flexible and elastic chemicals, are used widely as plasticizers, particularly in products made of polyvinyl chloride (PVC) (Niu et al., 2014). They are found in a wide variety of daily products including children’s toys, pesticides, food packaging, household furnishings, personal care products, medicines and even foodstuffs (Niu et al., 2014; Skrbic et al., 2016). One hundred fifty million tons of plastic is manufactured globally every year and PAEs are consumed at a rate of 6.0 to 8.0 million tons annually (Niu et al., 2014). As a result, PAEs are widespread in soil, water, sediments and air (Zeng et al., 2008b). PAEs are classified as endocrine disruptors that pose health risks to both terrestrial and aquatic species (Zeng et al., 2008a; Niu et al., 2014). Exposure to these substances leads to various adverse health complications (Kozumbo et al., 1982; Kluwe, 1986). Six specific PAEs (dimethyl phthalate (DMP), diethyl phthalate (DEP), di-n-butyl phthalate (DnBP), benzyl butyl phthalate (BBP), di(2-ethylhexyl) phthalate (DEHP), di-n-octyl phthalate (DnOP) are highly toxic and are categorized as priority contaminants by the US Environmental Protection Agency (USEPA, 2013) and the European Union (EU) (Zeng et al., 2008b; Wu et al., 2012). Therefore, PAE contamination and propagation are often the subject of worldwide monitoring studies (Niu et al., 2014; Skrbic et al., 2016). Kaohsiung City is the most industrialized area in Taiwan. It has a population of around three million and is an important economic and industrial center in Asia. In Kaohsiung, PAEs have been found in river sediment (Lin et al., 2009), drinking water (Yang et al., 2014), soil (Kaewlaoyoong et al., 2018) and foodstuff (Wu et al., 2013). PAEs were also detected in human urine in studies performed following a scandal involving industries that used PAEs as a substitute of emulsifier to food products they manufactured (Chen et al., 2016). Since that time, PAE contamination and associated health risk have been a major concern of Kaohsiung City government and its citizens (Yang et al., 2014). A large number of studies have investigated PAEs in indoor dust (Bornehag et al., 2005; Hwang et al., 2008; Orecchio et al., 2013; Zhang et al., 2013; Ait Bamai et al., 2014; Jeon et al., 2016; Albar et al., 2017; Larsson et al., 2017; Subedi et al., 2017), and human incidences of PAEs from indoor contamination have been reported (Pei et al., 2013; Tran et al., 2016; Albar et al., 2017).