ArticlePublisher preview available

Energy consumption, air pollution, and public health in China: based on the Two-Stage Dynamic Undesirable DEA model

Authors:
Hang Lin
Hang Lin
Hang Lin
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Huangxin Chen at Fujian Normal University
Huangxin Chen
Lin Zhang
Lin Zhang
Lin Zhang
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Youjia Luo
Youjia Luo
Youjia Luo
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Show all 6 authorsHide
Read publisher preview
Download citation
To read the full-text of this research, you can request a copy directly from the authors.

Abstract and Figures

The rapid development of China’s economy has largely relied on energy consumption, which has caused serious air pollution and affected public health, and economic development, energy consumption, air pollution, and public health have nowadays become the focus of academic attention. However, the previous literature failed to consider undesirable output when constructing the Dynamic Network DEA model to study the efficiencies of energy consumption, air pollution, and public health. As a result, past studies did not employ those three issues in a structure to effectively reflect and solve the problems. Therefore, this paper constructs the Two-Stage Dynamic Undesirable DEA model and puts energy consumption, air pollution, and public health into the same framework in order to fill the gap in the literature. Findings show that the production consumption efficiency stage is better than the health protection stage, and that the efficiency values of variables vary significantly in different regions. The efficiency of tumor and tuberculosis is the lowest, with oil consumption and birthrate efficiencies are the best, followed by coal, nitrogen oxide (NOx), and dust efficiencies. Coal efficiency exhibits a fluctuating downward trend, whereas the efficiencies of electricity, air pollutants, tuberculosis, and tumor tend to fluctuate upwards during the research period. In consideration of the varying performances of different regions in the two stages, we put forward suggestions based on these findings to improve the efficiencies of energy, environment, and public health in China.
Figures - available from: Air Quality Atmosphere & Health
This content is subject to copyright. Terms and conditions apply.
A preview of this full-text is provided by Springer Nature.
Learn more
Content available from Air Quality Atmosphere & Health
This content is subject to copyright. Terms and conditions apply.
Energy consumption, air pollution, and public health in China: based
on the Two-Stage Dynamic Undesirable DEA model
Hang Lin
1
&Huangxin Chen
1
&Lin Zhang
1
&Youjia Luo
1
&Yi Shi
1
&Wenjie Zou
1
Received: 31 December 2020 /Accepted: 7 April 2021
#Springer Nature B.V. 2021
Abstract
The rapid development of China’s economy has largely relied on energyconsumption, which has caused serious air pollution and
affected public health, and economic development, energy consumption, air pollution, and public health have nowadays become
the focus of academic attention. However, the previous literature failed to consider undesirable output when constructing the
Dynamic Network DEA model to study the efficiencies of energy consumption, air pollution, and public health. As a result, past
studies did not employ those three issues in a structure to effectively reflect and solve the problems. Therefore, this paper
constructs the Two-Stage Dynamic Undesirable DEA model and puts energy consumption, air pollution, and public health into
the same framework in order to fill the gap in the literature. Findings show that the production consumption efficiency stage is
better than the health protection stage, and that the efficiency values of variables vary significantly in different regions. The
efficiency of tumor and tuberculosis is the lowest, with oil consumption and birthrate efficiencies are the best, followed by coal,
nitrogen oxide (NO
x
), and dust efficiencies. Coal efficiency exhibits a fluctuating downward trend, whereas the efficiencies of
electricity, air pollutants, tuberculosis, and tumor tend to fluctuate upwards during the research period. In consideration of the
varying performances of different regions in the two stages, we put forward suggestions based on these findings to improve the
efficiencies of energy, environment, and public health in China.
Keywords Energy efficiency .Pollution efficiency .Health efficiency .Two-Stage Dynamic Undesirable DEA model
Introduction
Ever since the Industrial Revolution, energy has increasingly
become an important driving force for global economic and
social progress, but excessive energy consumption also aggra-
vates the emissions of pollutants, causing bad air quality and
threatening public health. The World Health Organization
(WHO) and United Nations Environment Program (UNEP)
since the 1980s have established an air pollution monitoring
networkcovering35citiesindevelopingcountriesandmore
than 50 cities in developed countries to help solve the issues
caused by air pollution.
China’s rapid economic development in the new millenni-
um has led to increasing energy consumption. In 2017, do-
mestic per capita energy consumption reached 3235 kg (stan-
dard coal) compared to 2000 kg (standard coal) in 2005. The
country’s energy consumption is dominated by coal, oil, and
electricity, which easily produce an immense amount of air
pollutants such as carbon monoxide, sulfur dioxide (SO
2
), and
dust. These air pollutants are prone to enter the human body
through the respiratory system and further increase the risk of
major diseases such as tumor and tuberculosis. According to
the China Health and Health Statistics Yearbook, from 2013
to 2018 the number of emergency cases of tuberculosis and
tumors in China increased by 32.8% and 72.8%, respectively.
In addition, China’s national average number of hospital visits
of residents (time) rose from 5.38 in 2013 to 6.23 in 2019.
H.L. and H.C. contributed equally to this work and should be considered
as co-first authors.
*Wenjie Zou
fjgt263@fjnu.edu.cn
Hang Lin
lh15394412625@163.com
Huangxin Chen
qbx20180005@yjs.fjnu.edu.cn
Lin Zhang
Zhanglin_Daniel@163.com
Youjia Luo
tmppchristina@163.com
Yi Shi
shiyi580231@163.com
1
School of Economics, Fujian Normal University,
Fuzhou 350108, China
https://doi.org/10.1007/s11869-021-01025-7
/ Published online: 24 April 2021
Air Quality, Atmosphere & Health (2021) 14:1349–1364
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... The economic development driven by extensive energy consumption has led to a continuous decline in the resource and environmental system capacity, manifested directly as deteriorating air quality and energy shortages (Lin et al. 2021). Since the beginning of the 21st century, extensive energy consumption has resulted in nearly 90% of the global population being exposed to long-term air pollution exceeding the standard limits. ...
... Numerous studies have shown that the conflict between energy demand and supply is escalating, and energy shortages have become an inevitable key issue impeding global economic development (Khan et al. 2016;Amri 2017). Air pollution and energy shortages have become common issues that hinder global economic sustainability (Wunderlich and Martinez 2018; Lin et al. 2021). ...
Energy and air? The impact of energy efficiency improvement on air quality in China
Article
Full-text available
  • Jul 2023
  • ENVIRON SCI POLLUT R
The global economic growth is hindered by resources shortage, energy demand, air pollution and climate. Energy efficiency can reduce some pollutants while potentially increase others. This study refers to sulfur dioxide (SO2), nitrogen oxides (NOx), and dust and smoke (DS) as primary pollutants to distinguish it from secondary ones. The influence of energy efficiency, socioeconomic, and natural climatic factors on air quality is analyzed under the theory of STIRPAT. It is highly coupled between energy efficiency and the spatial distribution of air quality. Increased energy efficiency can improve air quality by reducing SO2 and NOx, but the impact on DS is insignificant. Air pollutants decrease by about 0.531% for every 1% increase in temperature and 0.105% for every 1% increase in precipitation. Consumption will reduce air pollution, and there is an inverted U-shaped relationship between population density, economic scale, urbanization, technology innovation, and air pollution. It is worth mentioning that this work adds temperature and precipitation to the STIRPAT as natural climatic factors, analyzing the impact of energy efficiency on air pollution under the two-factor restrictions of socioeconomic and natural climatic factors. Finally, management suggestions are made to improve air quality.
View
... The region's dynamic coastal conditions make it an ideal case for testing data-driven resilience classification models, supporting applications in coastal risk management, climate adaptation, and sustainable urban development. Integrating multi-sensor data fusion and ML within this geographic context enhances the predictive capabilities of resilience assessment, offering valuable insights for urban planners, policymakers, and disaster response strategies [50], [54]. ...
Multi-Sensor Remote Sensing and AI-Driven Analysis for Coastal and Urban Resilience Classification
Article
Full-text available
  • Jan 2025
  • IEEE J-STARS
Urban resilience is essential for cities to endure and adjust to environmental and socioeconomic upheavals. The static indicators and rule-based spatial frameworks that are the mainstays of traditional resilience assessment models frequently fall short of capturing the dynamic character of coastal and urban resilience. This paper suggests a deep learning-based categorization framework for identifying resilience levels in urban and coastal settings by combining Long Short-Term Memory (LSTM) networks with multi-sensor remote sensing data. The Copernicus Marine Data Service's spatiotemporal ocean physics data, namely the eastward (uo) and northward (vo) seawater velocity, are used in the model to increase the precision of resilience evaluations. The methodology includes a multi-step deep learning pipeline, incorporating data preprocessing, feature extraction, class balancing with SMOTE, and LSTM-based classification. The proposed LSTM model is optimized to enhance performance with dropout regularisation (0.3), an Adam optimizer (learning rate = 0.0003), and class weighting strategies. The model is evaluated using accuracy, F1-score, confusion matrices, and loss curves, ensuring reliable classification across different resilience categories. Results indicate that the framework achieves high classification accuracy (91.5%), demonstrating superior performance compared to traditional machine learning approaches. Regarding multi-sensor fusion and deep learning, this study provides a scalable, adaptive, and data-driven solution for resilience classification, supporting climate adaptation strategies, disaster risk management, and sustainable urban development. The proposed methodology offers a robust tool for policymakers and urban planners, enabling more effective resilience monitoring and decision-making in rapidly evolving urban and coastal environments.
View
... Lung diseases are caused by CO 2 and SO 2 [4]. Thus, air pollution worsens health issues [5]. The World Health Organization (WHO) estimates that air pollution kills three million people each year [6]. ...
Exploring the Roles of Education, Renewable Energy, and Global Warming on Health Expenditures
Article
Full-text available
  • Sep 2023
Renewable energy sources—which are available in abundance all around us and are provided by the sun, wind, water, waste, and heat from the Earth—are replenished by nature and emit little to no greenhouse gases or pollutants into the air. This paper builds upon a preceding study that examined beliefs, perceptions, and attitudes regarding renewable energy technologies. In this study, we examine the implications renewable energy sources may have on science, technology policies, and education. This study embraced a sequential mixed-methods methodology to accomplish its objectives. The primary goal of this study was to ascertain the impact of global warming, education, and renewable energy on healthcare expenditure. In order to determine the impact of renewable energy on health care expenditure, the present research study coupled renewable energy with gross domestic product (GDP). Based on the long-term outcomes derived from our Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) estimators, GDP, renewable energy, and education were found to be adversely correlated with healthcare expenditure. To collect data, we conducted interview sessions, which were subsequently complemented by a survey that was completed by 400 respondents (specifically chosen research participants). We then conducted thematic analyses. The findings of this study underscore a compelling inverse relationship linking GDP, renewable energy integration, and education with healthcare spending. Regions displaying lower healthcare outlays are seemingly less strained ecologically due to their judicious utilization of renewable energy sources. Furthermore, based on our findings, a noteworthy correlation between healthcare expenditure and global warming was observed, underscoring the potential escalation of financial burdens with intensifying climate shifts. In light of our findings, advocating for the amplification of renewable energy deployment emerges as a prudent strategy to fortify public health while mitigating healthcare expenses. Augmenting investments in education acts as a pivotal lever to steer sustainable growth. It is noteworthy that the survey participants’ level of familiarity with renewable energy technology was not found to be connected to their educational backgrounds, revealing a disparity in knowledge. The prevailing unfamiliarity with sustainability principles among the respondents underscores the need for widespread awareness initiatives. This study acts as a holistic exploration of the ramifications of renewable energy on healthcare expenditure; this is intertwined with the complex tapestry of global warming and education. The implications of renewable energy reverberate across policy and practice, accentuating the urgency of sustainable energy adoption for the betterment of public health and economic resilience. Future studies should focus on conducting more nuanced assessments of socio-economic aspects and generate strategies for bridging knowledge gaps among diverse stakeholders.
View
... The links PM2.5, SO 2 , and NO x are from (Zhang et al., 2018a;Lin et al., 2021). The carry-over variable for multiple periods is government health expenditures, referring to (Zhang et al., 2022). ...
The dynamic relationship among economic development, air pollution, and health production in China: the DNSBM efficiency model
Article
Full-text available
  • Jul 2023
China’s economy has grown rapidly in response to the adoption of a high-energy, high-emissions development model, which has led to varying degrees of air pollution; moreover, the corresponding health damage has become a major concern for the public. Studies of the interrelationships between the economy, air pollution, and health often use empirical methods such as regression analysis, to explore the impacts of economic growth and air pollution, human health or air pollution on human health in isolation, and they rarely explore the interactions between the economy, air pollution, and health in terms of efficiency (i.e., the maximum output per unit of input resources that can be produced). Thus, this study constructed a Dynamic Network SBM efficiency model that unifies the production of economic development, air pollution, and health into a single framework. The article reports the findings of a comprehensive study of economic development, air pollution, and health management data for 30 Chinese provinces from 2015 to 2020. The results show that: 1) the overall efficiency (0.693) of China’s two stages from economic production input to healthy output from 2015 to 2020 is low, and there is much room for improvement; 2) the efficiency of economic development (0.729) is higher than the efficiency of health production (0.657), indicating that the health production stage needs to be strengthened; 3) during the study period, China’s PM2.5, SO2, and NOx control efficiency was low, at 0.786, 0.710, and 0.718, respectively, indicating that more effective measures are needed to improve environmental efficiency; and 4) there are significant differences in economic, environmental, and health efficiency across regions. China’s developed eastern provinces are more efficient in terms of economic development, health production, and air pollution control. In contrast, the central and western provinces, which are relatively backward in their economic development, are less efficient. This means that each province should implement sound policies based on its own assessment to promote sustainable economic development while enhancing air pollution mitigation and health promotion.
View
... DEA, SBM-DEA, and TOPSIS are some of these methods commonly used in energy-related studies (Çam, Karataş, and Lopcu 2022;Djordjević and Krmac 2019;Liu et al. 2013). In particular, DEA and its derivatives, as well as all methods of multi-criteria decision making, have been used in many areas of research, such as farm eco-efficiency (Picazo-Tadeo, Gómez-Limón, and Reig-Martínez 2011;Rybaczewska-Błażejowska and Gierulski 2018), healthcare systems and their efficiencies (Ahmed et al. 2019;Asandului, Roman, and Fatulescu 2014), effectiveness and performance of online banking systems (Faccia and Cavaliere 2021;Ho and Wu 2009), measuring innovation and its effectiveness (Bresciani et al. 2021;Zhong, Li, and Wang 2021), calculating airline efficiency (Law 2022;Ngo and Tsui 2021), calculating health and hospital efficiency (Kohl et al. 2019;Yaya et al. 2020), air pollution studies (Lin et al. 2021;Moutinho, Madaleno, and Macedo 2020), etc., and energy efficiency (Alp, Sözen, and Kazancıoğlu 2013;Chien and Hu 2007;Honma and Hu 2008;Hu and Kao 2007;Hu and Wang 2006;Wu, Cao, and Liu 2014). Energy efficiency studies cover a wide range of countries, from China to Japan and from Korea to the G-20 countries (Amowine et al. 2019;Cui et al. 2014;Dogan and Tugcu 2015;Fidanoski, Simeonovski, and Cvetkoska 2021;Kim et al. 2015;Lee, Hu, and Kao 2011;Liu and Sun 2022;Moon and Min 2020;Mukherjee 2008;Tachega et al. 2021;Wang, Le, and Nguyen 2019;Zalaghi et al. 2021;Zhang and Chen 2022;Zheng 2021;Zhou and Xu 2022). ...
Investigating the energy efficiency determinants in EU countries by using multi-criteria decision analysis and the Tobit regression model
Article
Full-text available
  • Jul 2023
This study investigates the effects of several contextual variables, includingrenewable energy intensity, capital stock per labor, natural resource rent, theshare of imported energy in total energy consumption, the ratio of carbonemissions to GDP, population, and energy production on energy efficiency inEU countries. While Tobit regression is used to examine the effects of con-textual variables on energy efficiency, Data Envelopment Analysis (DEA),Slack-Based Data Envelopment Analysis (SBM-DEA), and Technique forOrder of Preference by Similarity to the Ideal Solution (TOPSIS) are used tocalculate energy efficiency values of countries. Four different Tobit regres-sion models are estimated as a function of the censored value for the energyefficiency series. The results show that renewable energy intensity, the ratioof carbon emissions to GDP, and population size have negative effects onenergy efficiency. In contrast, regardless of the efficiency level, the share ofimported energy in total energy consumption, total energy production,capital stock per labor, and technological progress have positive effects onenergy efficiency.
View
... Balla and Gleason (2020) demonstrated that increasing the availability of proven vehicle safety technologies in Latin America and the Caribbean would have large gains. In China, a top emitter, Luo et al. (2021) found that vehicle-induced risk accounted for 12.5% and 22.2% of PM2.5and O 3 -induced deaths in China. According to Wang et al. (2020), the health benefits of vehicle emission control programs in China were a major concern. ...
A new approach for vehicle-health system measurement by network data envelopment analysis and an application in the USA
Article
Full-text available
  • Apr 2023
  • Environ Dev Sustain
Public health and climate change mitigation are strongly related to the efficiency of a unified vehicle-health system. To date, however, the existing DEA studies have not incorporated a robust set of transportation and public health indicators to evaluate the performance of the vehicle-health system. Our approach maps the relationship between transportation and health indicators in a network DEA framework. In addition, this study develops a new methodological framework for identifying stage priorities for decision-makers, peer evaluation and an understanding of the effects of policy in a comprehensive vehicle-health production system. This study empirically evaluates indicators of vehicle use and their effects on public health for each US state. The main conclusions are summarized: First, the network DEA and cross-efficiency network DEA models have significant methodological differences, highlighting the importance of model selection for empirical analysis. Second, US states exhibit enormous disparities in their prior strategies. The relative weights of the vehicle use stage and health outcomes stage confirm differing levels of importance attached to either stage. Third, group heterogeneity and technology inequality are observed among US states. Both intra-group and inter-group inequalities drive technology inequality. Moreover, considerable heterogeneity exists in the intra-group decomposition of the overall efficiency Theil index among the three network DEA models. Finally, promoting a safe and integrated transportation network improves vehicle-health performance, implying that strategic transportation policies hold significant potential for improving public health.
View
... [25] developed a dynamic DEA method that can identify changes in system efficiencies better than previous approach. Similar literature includes [26][27][28][29][30]. ...
View
Assessing China's energy‐related efficiency towards SDG7 and influencing factors
Article
With scholars paying increasing attention to energy sustainability, integrating energy sustainable utilization into the energy‐related assessment framework is particularly useful for achieving SDG7. This study empirically assesses the energy‐related efficiency ( EE ) comprised of the energy utilize stage and energy sustainable stage in China using a dynamic two‐stage data envelopment analysis model, and its influencing factors including digital economy with fixed‐effect models. The findings reveal a slight downward trend of EE from 2011 to 2020, primarily due to the poor performance of energy utilize stage. The subregional analysis illustrates that increasing clean energy use (CEU) is more beneficial to energy efficiency than reducing carbon emissions (CER). Dagum Gini coefficients indicate a growing disparity between regions. Additionally, a “U‐shaped” relationship between digital economy and EE is found, but two‐thirds provinces of China have not yet surpassed the turning point. Moreover, different degrees of CEU and CER will influence the impact of digital economy on EE . Environmental regulations and green technology innovation play positive moderating roles in the “U‐shaped” relationship. This study recommends to stimulate the energy sustainable use through higher adoption of clean energy sources and carbon emissions reduction, as well as highlight the green value of the digital economy.
View
View
View
Spatial Correlation, Influencing Factors and Environmental Supervision Mechanism Construction of Atmospheric Pollution: An Empirical Study on SO2 Emissions in China
Article
Full-text available
  • Mar 2019
In order to study the present situation, problems, reasons of environmental pollution and put forward some policy suggestions. This paper analyzes spatial correlation, influencing factors and regulatory tools of air pollution in 30 provinces on the Chinese mainland from 2006–2015. The results of exploratory spatial data analysis (ESDA) show that SO2 emissions have obvious positive spatial correlations, and atmospheric pollution in China shows obvious spatial overflow effects and spatial agglomeration characteristics. On this basis, the present study analyzes the impact of seven socioeconomical (SE) factors and seven policy tools on air pollution by constructing a STIRPAT model and a spatial econometric model. We found that population pressure, affluence, energy consumption (EC), industrial development level (ID), urbanization level (UL) and the degree of marketization can significantly promote the increase of SO2 emissions, but technology and governmental supervision of the environment have significant inhibitory effects. The reason why China’s air pollution is curbed at present is because the government has adopted a large number of powerful command-controlled supervision measures, to a large extent. Air pollution treatment is like a government-led “political movement”. The effect of the market is relatively weak and public force has not been effectively exerted. In the future, a comprehensive use of a variety of regulation tools is needed, as well as encouraging the public to participate, strengthening the supervision of third parties and building a diversified and all-encompassing supervision mechanism.
View
Spatial-temporal characteristics and influencing factors of atmospheric environmental efficiency in China
Article
Full-text available
  • Mar 2021
  • ENVIRON SCI POLLUT R
This research constructs a super efficiency slack-based measure (SBM) model based on the Malmquist-Luenberger (ML) index to analyze the atmospheric environmental efficiency (AEE) of 30 provinces in China from 2000 to 2016 and explores the spatial and temporal differences of AEE by using the coefficient of variation method. This paper further analyzes the internal influencing factors of AEE via the ML index decomposition approach and establishes a panel data regression model to explore AEE’s influencing factors in China. The results show some regional differences of the AEE level in China, with it the best in the eastern region and followed in order by the western and central region, and these differences exhibit an increasing trend year by year. During the study period, the development trend of AEE in China is similar to that in the eastern and western regions, showing a “W” shape, where in the central region it has a “U” pattern. The conclusion is that technical progress (TC) is the dominant factor affecting AEE, technical efficiency (EC) fails to effectively promote AEE improvement, and TC and EC present varying degrees of influence and different directions of action in the regions. The analysis results show that the influence effect of economic development on AEE presents a “U” pattern of first declining and then rising. The degree of China opening up to the outside world and its carbon dioxide emissions intensity have significant negative effects on AEE, whereas the increase of pollution control input effectively improves AEE.
View
The impact of air pollution on urban residents’ health expenditure: spatial evidence from Yangtze River Delta, China
Article
Full-text available
  • Mar 2021
Currently, preventing and controlling air pollution and inhibiting the excessively rapid growth of medical expenditures to reduce the living burden of residents have become a focus of general concern of society as a whole. It is of great significance to conduct an in-depth study of the relationship between air pollution and public health expenditure and clarify to what extent air pollution affects health expenditure. Thus, this paper adopted spatial econometric methods to measure the impact of air pollution on residents’ health expenditure via panel data from 16 core cities in the Yangtze River Delta in China from 2005 to 2017. The results indicate that (1) at present, the air pollution in the Yangtze River Delta core urban agglomeration is still relatively prominent and has obvious spatial aggregation phenomena, and the particulate matter 2.5 (PM2.5) concentration shows the characteristic of decreasing gradually from northwest to southeast; (2) air pollution is the main factor that promotes the increase in residents’ health expenditure; (3) the spatial spillover effect of air pollution is obvious, accounting for half of the total effect; and (4) the impact of government public services on residents’ health expenditure varies. Finally, this paper proposes corresponding policy implications based on the findings.
View
Regional differences of air pollution in China: comparison of clustering analysis and systematic clustering methods of panel data based on gray relational analysis
Article
Full-text available
  • Oct 2020
Ecological environment deterioration caused by air pollution (AP) seriously threatens human life and health, with academic circles focusing on how to improve AP control efficiency, promote regional collaborative regulatory control of AP, and ensure the safety of human life. This research thus uses time-based systematic clustering of cross-sectional data and gray correlation clustering of panel data to analyze AP situations and regional differences (RD) via AP index data of 31 provinces and cities in China from 2014 to 2018. The results show that the air quality of these provinces and cities has improved and that the concentration of pollutants has declined to varying degrees, but AP in Beijing, Tianjin, and other places is still more serious. While the effect of AP improvement in some areas is not distinct, the trend of regional AP continues to be quite serious. This paper reveals current RD of AP in China and offers some suggestions and recommendations on pollution control, such as further optimizing the industrial structure and energy consumption structure and improving people’s concept of ecological civilization.
View
Research on the Regional Differences and Influencing Factors of the Innovation Efficiency of China’s High-Tech Industries: Based on a Shared Inputs Two-Stage Network DEA
Article
Full-text available
  • Apr 2020
Innovation ability has become one of the core elements in the pursuit of China’s green growth, and high-tech industries are playing a leading role in technological innovation in China. With the rapid development of China’s high-tech industries, their innovation efficiency has attracted widespread attention. This article aims to illustrate a shared inputs two-stage network Data Envelopment Analysis (DEA), to measure the innovation efficiency of high-tech industries in China’s 29 provinces from 1999 to 2018. The results indicate that there are obvious differences in the innovation efficiency of the provinces. The technology development efficiency, the technical transformation efficiency, and the overall innovation efficiency of the developed east coast provinces are generally higher than those of the backward central and western provinces. This article further applies the spatial econometrics model to analyze the factors influencing the innovation efficiency of high-tech industries. We have found that government support, R&D input intensity, industries aggregation, economic extroversion, and the level of development of the modern service industries cause varying degrees of impact on innovation efficiency.
View
The mediated role of complement C3 in PM2.5 exposure and type 2 diabetes mellitus: an elderly panel study in Beijing, China
Article
Full-text available
  • Nov 2019
  • ENVIRON SCI POLLUT R
Diabetes mellitus (DM) is a common chronic disease worldwide. Ambient air pollution has long been proven to be associated with type 2 diabetes mellitus (T2DM) progression, but the underlying mechanism is not clear yet. In addition, previous studies mainly focused on the prevention of healthy people against the incidence of T2DM. We designed a panel study including two follow-ups and enrolled 39 patients with T2DM living in Beijing. Linear mixed model was fitted to assess the association between two pairs of variables (ambient air pollution exposure and C3 levels, ambient air pollution exposures and T2DM index). Mediation analysis of C3 between ambient air pollution exposure and indicators of T2DM progression was conducted. We found that PM2.5 exposures is are negatively associated with serum complement C3. Given that C3 might act as a protector of pancreas β cell, PM2.5 exposures could accelerate disease in T2DM populations. No mediation effects were found. This study reveals that exposures to PM2.5 can cause progression of diseases among T2DM populations.
View
Energy and Environmental Efficiency in Different Chinese Regions
Article
Full-text available
  • Feb 2019
China has become the second-largest economy in the world; however, the price of its rapid economic development has been a rise in serious environmental pollution, with air quality being a major public issue in many regions. However, few previous energy and environmental sustainability studies have included the Air Quality Index (AOI) and in particular CO2 and PM2.5 emissions in their calculations and few have included regional differences, as these are difficult to describe using radial and non-radial methods. In this paper, DEA (Data Envelopment Analysis) is used to assess the energy and economic efficiencies of Chinese provinces and cities, in which the environmental pollution source variable is CO2, and the main methods applied are radial (CCR or BCC) and non-radial SBM (Slacks Based Measures). Different from past studies, this study used both a Meta Undesirable EBM (Epsilon-Based measure) method to overcome the radial and non-radial errors and geographical differences and AQI environmental pollution indicators to accurately assess the economic, energy, and environmental efficiencies. It was found that: (1) Guangzhou and Shanghai had the best four-year efficiencies, (2) the energy efficiency differences in each city were large and there was a significant need for improvements, (3) the GDP efficiencies in each city were high, indicating that all cities had strong economic development, (4) the CO2 efficiencies indicated that around half the cities had had sustained improvements, (5) the AQI efficiencies in each city were low and there was a significant need for improvement, and (6) the technological differences between the cities were large, with the efficiencies in the high-income cities being much higher than in the low-income cities.
View
Evaluating the Impact of Clean Energy Consumption and Factor Allocation on China’s Air Pollution: A Spatial Econometric Approach
Article
  • Dec 2019
  • ENERGY
China’s air pollution has become a widespread concern in the academic world, but there are few studies based on spatial measurement methods to quantify the impact of clean energy consumption and factor allocation on a variety of pollutants. Based on an exploratory spatial data analysis, a spatial Durbin model and extended Cobb-Douglas production function (C-D production function) are used to study the direct, indirect and total effects of clean energy consumption and element allocation on China’s air pollution emissions. The exploratory spatial data analysis results showed that the three emissions had significant agglomeration effects, and the spatial aggregation patterns of the emissions were similar to the patterns of fossil energy consumption spatial aggregation. The spatial Durbin model estimation results showed that the clean energy consumption proportion and factor allocation of energy and labour inhibited air pollution emissions. The spatial spillover effect was greater than the direct effect. The fossil energy structure and factor allocation of energy and capital stock were positively related to air pollution emissions. These findings help to formulate regional industrial policies and energy policies and contribute to the governance of air pollution and the sustainable development of economic, environmental and energy resources.
View
The dynamic network dea model
Chapter
  • Oct 2016
The dynamic network SBM model is a composite of network DEA and dynamic DEA with a slacks-based measure approach. Vertically, it deals with multiple divisions connected by links of the network structure within each period and, horizontally, it combines network structures by means of carry-over activities between two succeeding periods.
View
Differential effects of size-specific particulate matter on emergency department visits for respiratory and cardiovascular diseases in Guangzhou, China
Article
  • Aug 2018
  • ENVIRON POLLUT
Background: Studies differentiating the cardiorespiratory morbidity effects of PM2.5, PM10, and PM2.5~10 (i.e. coarse PM or PMc) are still limited and inconsistent. Objective: To estimate the acute, cumulative, and harvesting effects of exposure to the three size-specific PM on cardiorespiratory morbidity, and their concentration-response relations. Methods: A total of 6,727,439 emergency department (ED) visits were collected from 16 public teaching hospitals in Guangzhou, from January 1st 2012 to December 31st 2015, among which over 2.1 million were asthma, COPD, pneumonia, respiratory tract infection (RTI), hypertension, stroke, and coronary heart disease (CHD). Distributed lag non-linear models (DLNM) was used to estimate the associations between the three size-specific PM and ED visits for the cardiovascular diseases. Long-term trends, seasonality, influenza epidemics, meteorological factors, and other gas pollutants, including SO2, NO2, and O3, were adjusted. We stratified the analyses by gender and age. Results: Elevated PM2.5 and PM10 were significantly associated with increased ED visits for pneumonia, RTI, and CHD at both lag0 and lag0-3. A 10 µg/m3 increment of PMc (at lag0-14) was estimated to increase ED visits for pneumonia by 6.32% (95% CI, 4.19, 8.49) and for RTI by 4.72% (95% CI, 3.81, 5.63), respectively. PMc showed stronger cumulative effects on asthma in children than elderly. We observed significant harvesting effects (i.e. morbidity displacements) of the three size-specific PM on respiratory but very little on cardiovascular ED visits. The concentration-response curves suggested non-linear relations between exposures to the three different sizes of PM and respiratory morbidity. Conclusions: Overall, the three size-specific PM demonstrated distinct acute and cumulative effects on the cardiorespiratory diseases. PM2.5 and PMc would have significant effects on pneumonia and RTI. Strategies should be considered to further reduce levels of ambient PM2.5 and PMc.
View