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Urban spatial structure evolution and smog management in China: A re-examination using nonparametric panel model
Abstract
Based on the modified DMSP night-time light composite data, this paper constructs an urban polycentric index, then uses a time-varying panel model to estimate the empirical equations of smog pollution. The empirical results show that: (1) with the long-term steady improvement of China’s per capita income, the income growth and air quality have shown a clear coordinated development trend (2) the regional multi-centered development has a significant mechanism of promoting the rise and then the fall of smog pollution (3) China’s secondary industry and transportation industry no longer have significant pollution-promoting effect at present, and are upgrading to environment-friendly industries. Besides, this study makes some additional contributions: On the one hand, the correction of night-time light data can provide more accurate data processing criterion for related research. On the other, the time-varying panel estimation further explains the complex mechanism between economic variables and smog pollution as well as reveals the origin of the instability of the EKC hypothesis. These improvements may provide an important academic reference for the subsequent researches and a general empirical evidence for policy makers.
... revealing the instability of the relationship (Liu et al., 2021a. For instance, Yin et al. (2022) demonstrated the existence of a dual relationship in the pollution-intensive industries, where economic and environmental factors mutually reinforce and constrain each other. ...
... In consideration of the data on energy consumption, this study utilizes energy consumption data from the transport and postal services industry to calculate. We draw upon the findings of previous studies Liu et al., 2021aLiu et al., , 2021bMa et al., 2018) and employ data for eight types of fuels to calculate the CO 2 emissions. The calculation method is as follows: ...
... According to previous research (Cheng & Hu, 2023;Khan et al., 2022;Liu et al., 2021aLiu et al., , 2021bZheng et al., 2021), specific descriptions of the control variables selected in this study are as follows: ...
Promoting the coordination between the economic and environmental aspects of the transport sector can reduce carbon emissions and achieve low-carbon sustainable development. With the implementation of China’s dual carbon goals, there is a need for further exploration into improving the coordination performance of the transport sector. This study utilizes data from 2009 to 2019 in Chinese provinces and employs a two-stage network data envelopment analysis and coupling coordination degree model to measure the economic–environmental coordination (EEC) of the transport sector. It also visualizes the spatiotemporal evolution characteristics. Furthermore, we utilize a spatial Durbin model to analyze the multifaceted and complex factors influencing EEC from a spatial effects perspective. The results indicate that: (1) Economic efficiency consistently outperforms environmental efficiency in each province. (2) EEC performs better in the east than in the west and center. (3) Population density, energy intensity, and informatization are key factors hindering the improvement of EEC and it should be noted that influencing factors differ across regions. (4) EEC indeed has spatial effect, highlighting the importance of considering regional interdependence and mutual influence. This study underscores the crucial link between low-carbon construction and coordinated development, offering a novel avenue for the transformation of the transport sector. It also provides valuable management insights for enterprises and governments seeking new development opportunities.
... The production and concentration distribution of air pollutants are affected by many factors, and each air pollutant has a different source of pollution [4]. Among them, fossil fuel combustion, industrial production, transportation, and economic development heavily impact the generation and distribution of air pollutants [5][6][7][8][9]. Reducing pollutant emissions is key to improving air quality [10,11]. ...
... Given that the urban spatial structure directly affects local meteorological conditions, such as urban temperature and ventilation, it also substantially impacts urban air quality [12][13][14][15]. The reported studies on influencing factors for urban air pollutants mainly use geographical detectors [16], multivariate Moran models [17], nonparametric panel models [5], and correlation analysis. The intensity and direction of air pollution are affected differently by the spatial structure of cities with different levels of size and economy [18]. ...
The spatial distribution pattern of urban spatial structure affects air flow and local meteorological conditions, which in turn influence the diffusion of air pollutants. This study built the urban spatial structure index system based on DEM, urban road networks, and big data. The ordinary kriging interpolation method was used to analyze the spatial distribution of gaseous pollutant concentrations in Jinan City. Correlation analysis, stepwise regression analysis, and bivariate global spatial autocorrelation analysis were used to study the influence of the urban spatial structure index on the spatial distribution of gaseous pollutant concentration. The main conclusions were as follows: (1) Evident spatial and temporal differences were observed in the concentration distribution of gaseous pollutants in Jinan. The spatial distribution of NO2 and CO concentrations showed a gradual decrease from north to south. Spatial heterogeneity was observed in the distribution of SO2 and O3 concentrations. (2) The urban spatial structure indicators had varying effects on the spatial distribution of different gaseous pollutant concentrations. The important factors that influenced the spatial distribution of urban gaseous pollutant concentrations included terrain elevation, building density, building volume, and floor area ratio. The greater the terrain undulation, the denser the building distribution, the greater the difference in building volume, and the greater the plot ratio, the greater the impact on the diffusion and spatial distribution of urban gaseous pollutants. (3) The spatial distribution of urban gaseous pollutant concentrations was significantly affected by the urban spatial structure indicators in the surrounding areas. Furthermore, the spatial distributions of NO2, SO2, CO, and O3 concentrations had a significant negative spatial correlation with the average DEM and standard deviation of the surrounding adjacent areas and a significant positive spatial correlation with the average and standard deviation of building height, standard deviation of building area, and building density in the surrounding adjacent areas (in June).
... Since 2013, smog pollution caused by a mixture of inhalable particles (Abbas et al., 2020;Abbas et al., 2022), PM 10 , and fine particulate matter PM 2.5 , has been rampant throughout China, causing serious concern for the Chinese government (Yuan et al., 2018;Liu et al., 2021). To protect the fundamental interests of the Chinese people and to ensure sound and sustainable development of the Chinese economy, the Central Committee of the Communist Party of China and the State Council have pinpointed the prevention and control of environmental pollution as a key task of improving people's livelihood and promoting high-quality economic development. ...
... As an unpleasant outcome of urbanization, smog pollution has a close bearing on the spatial development of cities (Clark et al., 2011;Han et al., 2020;Liu et al., 2021). Examined by the formation mechanism, smog pollution is decided by the pollutant concentration, atmospheric conditions and air moisture (Li et al., 2022). ...
The study examines the influence of the urban spatial structure on smog pollution and its mechanism of action using the panel data of Chinese cities from 2012 to 2017. Research suggests that the multi-center development of Chinese cities has aggravated regional smog pollution. Robustness test results support this finding. Results of the interaction term shed light on the fact that multi-center spatial development in backward regions can exert a greater influence on smog pollution. Meanwhile, suppose the research samples are divided by different zones. In that case, it can be observed that an inverted-U shape characterizes the multi-center development of regions of East China and smog pollution. This indicates that worsening smog pollution appears only as a periodical phenomenon of the pre-stage multi-center development of Chinese cities, which can be ushered into a turning point through certain paths. Analysis of the influence mechanism shows that the transport mode and the energy consumption intensity constitute two major channels for multi-center development of Chinese cities to affect regional smog pollution.
... Urbanisation, as a phenomenon closely associated with economic and social advancement, has garnered attention and its impact has been the subject of extensive discourse. Additionally, recent research has been conducted within the realm of urban and regional spatial structure, utilising the established model as a framework (Kang et al., 2016;Liu et al., 2021;Zhou & Wang, 2018). According to a literature review, the aggregation of economic activities in a given spatial structure can either enhance or reduce environmental sustainability, depending on the balance between positive and negative externalities of agglomeration economies. ...
Abstract
This article presents a systematic review of empirical studies on polycentric spatial structures at a regional scale in order to assess their effectiveness as prescriptive and normative models in spatial planning. The results show that very few studies have emphasised primarily the positive effects of polycentricity, while a large number have evaluated the performance of non-polycentric (monocentric) structures more positively. Our study shows that evaluating the effectiveness of polycentricity as a normative model is both theoretically and empirically challenging, and that polycentricity is still the subject of a research agenda with hypotheses that need to be tested. The findings indicate that polycentricity is not the superior model it has been frequently advertised as and that its effectiveness is significantly influenced by a range of factors relating to its political foundation, weak theoretical positioning, ambiguous conceptualisation, context dependence, and highly variable governance frameworks. The study recommends that scientific theorising of polycentricity should be aligned with close scrutiny of the relevant contexts to overcome its idealistic nature and lack of adaptability. The article cautions planners and policymakers against a sweeping promotion of polycentric development, as the implementation of this concept is not necessarily associated with fostering economic performance, social cohesion, and environmental sustainability.
Introduction
Over the last two decades, the utilisation of the concepts of “polycentrism”1 and “polycentric development” has noticeably increased in the strategies and plans of spatial development (Meijers, 2008a, Wang et al., 2022). These concepts have been recognised as key elements and served as both a scientific analytical framework for examining real-world phenomena and the politically important parts of normative goals and perspectives in spatial development policies (Harrison and Hoyler, 2014, Lambregts, 2009, Derudder et al., 2022, Hall and Pain, 2006, Taylor et al., 2008). To achieve objectives such as enhancing economic performance, promoting social cohesion, and advancing environmental sustainability, these concepts have been integrated into spatial development policies and plans at different levels (Burger and Meijers, 2012, Zhang et al., 2022). Historical recovery shows that the emergence of the concept of polycentricity in the 1990 s to promote the benefits of “European integration” is a significant departure from traditional approaches in spatial planning. In particular, the European Spatial Development Perspective Document (ESDP) introduced a new framework for considering the spatial organisation of cities and regions. ESDP advocates for the first time a balanced spatial structure that fosters cooperation and complementarity between cities and regions. This normative interpretation from polycentricity made it the most desirable model of spatial structure and a leading principle in achieving balanced regional development throughout the EU, making it the main objective of EU regional policy. Today, polycentric conflict remains a central theme in EU territorial cohesion policy, and its popularity has spread even beyond Europe. This concept in normative interpretation promises to reduce spatial inequalities, combat the negative effects of excessive concentration and social-spatial polarization, and increase the competitiveness and efficiency of economic production regardless of the spatial context. However, the popularity of the concept of polycentricity has led to its acceptance in planning circles, and its vague and ambiguous meanings have caused confusion and uncertainty in the conceptualization of polycentric development in the planning apparatus. Simultaneous polycentricity has quickly attracted the attention of academic circles and has become an important research topic. This issue has caused controversy between the scientific and political discourse of polycentricity. On one side, the absence of robust empirical evidence has led some researchers to concentrate on the potential and theoretical aspects of polycentricity rather than empirical facts and theories (Groth et al., 2011). On the other, politicians have established the foundation for the practical introduction of these concepts into countries' spatial planning documents by giving their approval. Planners and politicians, inspired by the adoption of polycentricity as a role model in certain regions, have taken active measures to implement it in other contexts through public intervention. Some researchers adopt a more cautious viewpoint to the claimed positive functions of polycentricity, opting to thoroughly evaluate the empirical evidence before reaching any conclusions. Viewing it as a matter of concern, Davoudi (2007), Meijers and Sandberg (2008), and Li et al. (2022a) have expressed their criticism of the political endorsement of polycentricity as a panacea for all problems. They posit that polycentricity may simply serve as a placebo, which could mean anything and potentially act as a pathogen, exacerbating existing problems. While polycentricity theoretically asserts to improve economic performance, enhance social cohesion, and promote environmental sustainability, these claims have yet to be substantiated through empirical evidence (Davoudi, 2003, Manole et al., 2020, Parr, 2004, Rauhut, 2017; B. A. S. Waterhout et al., 2005; W. Li, Sun, et al., 2018; Natalia & Heinrichs, 2020).
Despite researchers' involvement, the absence of theoretical perspectives to support polycentric development as a normative policy stance remains an issue. Its effects are also uncertain from an empirical viewpoint (Agarwal et al., 2012, Rauhut, 2017, Vandermotten et al., 2008). The questionability of polycentric development's multi-objective nature as a spatial planning key concept is apparent. The examination of the social, economic, and environmental impacts of polycentricity, including both potential advantages and drawbacks, has been insufficiently addressed or overlooked. The significance of this issue lies in the recognition of polycentricity as a desirable and normative model for spatial development, as it has the potential to serve as a guiding principle for political intervention (Cheng and Shaw, 2018, Rauhut, 2017, Waite, 2023). Camagni and Capello (2004) maintain that polycentricity can only be regarded as a new paradigm in spatial sciences when its exact meaning is fully defined, its theoretical economic logic is justified, and its empirical characteristics are distinguished from traditional spatial realities and processes. Davoudi (2003) believes that failing to promote and understand polycentricity as a model for spatial development will result in it becoming just another “idea” and not a sustainable model. Thus, to ensure its sustainability, polycentric development must undergo testing to determine its usefulness and validity (Meijers, 2008a). The idea of polycentricity is intricate, just like TV series that need a complex blend of strong storytelling, engaging characters, high production standards, skilled actors and agents, innovation, effective marketing, and critical acclaim to be successful. Polycentricity is akin to TV series in the sense that various factors are crucial and must be present for it to succeed. Consequently, supporting polycentricity based solely on idealistic and positive objectives is inadequate, as weak links in any of the necessary factors may lead to its failure. Thus, to maximise the potential of polycentric development in spatial planning, it's crucial to assess its implications and adopt well-informed versions of it instead of blindly copying it.
In the academic literature, the term “polycentric urban regions” (PURs) refers to a manifestation of the scaling of urban development and the formation of “regional urbanisation.” (Soja, 2015). These new spatial structures result from the merger of adjacent cities of similar size, which were previously separate but interlinked (Burger and Meijers, 2012, Meijers et al., 2018). PURs are characterised by a regional spatial structure in which multiple urban areas interact and cooperate (Brezzi & Veneri, 2015). These regions exhibit interdependent economies, labour markets, and shared infrastructures; thereby, they transform the constituent settlements into “overarching entities” through their connections (Liu et al., 2016). The prevalence of functional relationships between cities, such as the development of agglomeration economies and the absence of diseconomies associated with more concentrated forms of development, such as congestion and environmental pollution, contribute to the growing prominence of PURs. Additionally, the pursuit of more sustainable and balanced urban development across cities further supports the organisation of urban systems in a polycentric manner (Van den Berghe et al., 2022). Therefore, the promotion of PURs is seen as a means to improve economic performance, promote social cohesion, and enhance environmental sustainability.
The examination of polycentric urban structures on this scale has garnered attention from several researchers in the field. This has resulted in an increased focus on investigating the characteristics and impacts of such spatial structures. These evaluations offer a valuable contribution to the ongoing debate surrounding polycentric urban structures. However, the question of whether polycentricity is a desirable spatial structure remains unresolved, and obtaining answers to these fundamental and supplementary questions is crucial for informed political decision making (Li and Liu, 2018, Meijers and Burger, 2010, Zhang et al., 2017). In light of these considerations, the present study aims to systematically review the empirical evidence on polycentricity on a regional scale. It endeavours to answer two questions: 1) Does empirical evidence support the notions about the effectiveness of polycentric urban structures in terms of achieving their intended goals on a regional scale? 2) What factors influence the effectiveness of polycentricity as a prescriptive and normative model? To put it simply, this study's second question investigates the effectiveness of the prescriptive and normative model "polycentricity." Effectiveness in this sense is measured by how well the model achieves its predetermined goals and objectives in real-world scenarios. Evaluating the degree of effectiveness involves assessing the model's alignment with the values and requirements of stakeholders; how effectively it addresses the challenges and opportunities of the planning context; and how efficiently it achieves its intended outcomes. To put it in a different way, this study aims to make a novel contribution to the field by tackling two main objectives. First, it aims to explore the impact of polycentricity on economic performance, social cohesion, and environmental sustainability. Second, it conducts a critical evaluation of the factors that influence the effectiveness of polycentricity as a prescriptive and normative model in spatial planning. To this end, it conducts an in-depth examination of various perspectives to systematically outline and examine the potential of polycentricity to advance economic, social, and environmental progress, or alternatively, to determine why it may not be successful in doing so.
The study is structured as follows: Section 2 offers an overview of the literature review, followed by the explanation of the methodology in Section 3. Section 4 presents the analyses, which will be discussed in Section 5. Finally, Section 6 presents the conclusion.
Section snippets
Literature review
The concept of PURs can be viewed as a product of an ongoing global discourse among scholars in the fields of urban planning and geography, dating back several decades into the 20th century. It has been argued that the polycentric spatial structures are becoming the leading form of city-regions in the new millennium (Bartosiewicz and Marcinczak, 2022, Dieleman and Faludi, 1998, Kloosterman and Musterd, 2001, Phelps and Ozawa, 2003, Van Houtum and Lagendijk, 2001). Van Meeteren (2022) sheds
Material and methods
The study at hand sets out to gather empirical evidence of the effects of polycentricity and to evaluate the factors affecting the effectiveness of polycentricity as a prescriptive and normative model. This is accomplished through conducting a systematic literature review, which serves as the first endeavour to organise and consolidate the existing knowledge and to provide relevant insights to researchers, planners, and policymakers (Fink, 2014). The systematic literature review is a rigorous
Polycentricity and economic performance
The evaluations of polycentricity that are deemed most critical are often linked to economic performance. Theoretical logic suggests that transitioning from monocentricity to polycentricity in urban regions may lead to a higher economic performance, and researchers have attempted to establish this by constructing empirical evidence. To evaluate the impact of agglomeration economies on local labour productivity, most studies adopt models based on the Cobb-Douglas production function (Ciccone,
Discussion
This article aims to conduct a systematic review of the evaluations of polycentricity in the intercity (regional) scale and to critically analyse the factors affecting its effectiveness as a prescriptive and normative key model in spatial planning. The empirical studies in this field have analysed various aspects, including economic performance, environmental sustainability, social cohesion, and multi-objective evaluations. The selected studies were categorised into four groups: positive,
Conclusion
Contemporary spatial planning recognises polycentricity as a fundamental principle. Policymakers have emphasised the transformation of the concept of polycentricity from a mere descriptive and analytical model into a prescriptive and normative model aimed at promoting economic performance, social cohesion, and environmental sustainability. With the objective of assessing the evaluation of polycentric spatial structures at the regional scale through empirical evidence and determining the factors
CRediT authorship contribution statement
Hashem Dadashpoor: Conceptualization, Methodology, Writing – original draft, Writing – review & editing, Supervision. Abbas Doorudinia: Conceptualization, Methodology, Investigation, Writing – original draft. Abolfazl Meshkini: Conceptualization, Writing − review & editing.
Declaration of Competing Interest
The authors report no declarations of interest.
Acknowledgment
We would like to express our sincere appreciation and respect to the esteemed editor, Karl Fischer and anonymous reviewers for their diligent and thorough review of our manuscript. Their invaluable guidance and constructive feedback have significantly improved the quality of our work.
Hashem Dadashpoor is an associate professor at Tarbiat Modares University, Faculty of Art and Architecture, Department of Urban and Regional Planning. His research interests include urban and regional planning, urban and regional studies, spatial planning, land use planning, and planning theory.
References (362)
S. Angel et al.
The productivity of American cities: How densification, relocation, and greater mobility sustain the productive advantage of larger U.S. metropolitan labor markets
Cities
(2016)
B. Bartosiewicz et al.
Investigating polycentric urban regions: Different measures – Different results
Cities
(2020)
J.C. Brinkman
Congestion, agglomeration, and the structure of cities
Journal of Urban Economics
(2016)
M. Brülhart et al.
Agglomeration and growth: Cross-country evidence
Journal of Urban Economics
(2009)
D. Burgalassi et al.
Urban spatial structure and environmental emissions: A survey of the literature and some empirical evidence for Italian NUTS 3 regions
Cities
(2015)
M.J. Burger et al.
Heterogeneous development of metropolitan spatial structure: Evidence from commuting patterns in English and Welsh city-regions, 1981–2001
Cities
(2011)
R. Camagni et al.
The City Network Paradigm: Theory and Empirical Evidence
(2004)
D. Castells-Quintana
Malthus living in a slum: Urban concentration, infrastructure and economic growth
Journal of Urban Economics
(2017)
O. Cats et al.
Multi-modal network evolution in polycentric regions
Journal of Transport Geography
(2021)
J. Chen et al.
City size and urban labor productivity in China: New evidence from spatial city-level panel data analysis
Economic Systems
(2017)
... This typical air pollutant presents multiple negative aspects, such as environmental destruction and a negative impact on human health, acting as a precursor of secondary aerosols and producing serious phenomena such as haze, photochemical smog, and emissions of particulate matter with a diameter of less than 2.5 μm (PM 2.5 ). 1,2 The release of these harmful gaseous pollutants into the atmosphere has been identified as the fourth ranked threat to human health. 3 In the past decades, the design and manufacturing of cutting-edge photocatalytic materials has become a relevant and necessary challenge to degrade most of the possible pollutants. ...
... Urban spatial structure can influence the vertical and horizontal air flows in the cities, which will have a significant impact on the spatial distribution and dispersion paths of pollutants (Fang & Qu, 2018). The reported studies on influencing factors for urban air pollutants mainly use geographical detectors , multivariate Moran models (Yao et al., 2019), nonparametric panel models (Liu et al., 2021a), and correlation analysis. The intensity and direction of air pollution are affected differently by the spatial structure of cities with different levels of size and economy (Wang et al., 2021). ...
Buildings are the main component of urban, and their three-dimensional spatial patterns affect meteorological conditions and consequently, the spatial distribution of gaseous pollutants (CO, NO, NO2, and SO2). This study uses the Jinan Central District as the study area and constructs a building spatial distribution index system based on DEM, urban road network, and building big data. ANOVA and spatial regression models were used to study the effects of building spatial distribution indicators on the distribution of gaseous pollutants along with their spatial heterogeneity. The results showed that (1) the effects of most of spatial distribution indexes of building on the concentration distribution of the four gaseous pollutants were significant, with one-way ANOVA outcomes reaching a significance level of 0.01 or more. The DEM mean, building altitude, and their interaction with other building spatial distribution indicators are important factors affecting the distribution of gaseous pollutants; The interaction of other three-factor indicators did not have a significant effect on the distribution of gaseous pollutant concentrations. (2) The spatial distribution of CO and NO2 is mainly influenced by the indicators of the spatial distribution of buildings in this study unit, and the effects of CO and NO2 concentrations in adjacent study units are the result of the action of stochastic factors. The NO and SO2 concentrations are influenced by the spatial distribution index of buildings in this study unit, the neighborhood homogeneity index, and NO and SO2 concentrations. (3) Spatial heterogeneity was observed in the effects of building spatial distribution indicators on the concentrations of different pollutants. The GWR models constructed using CO and NO concentrations and building spatial distribution indicators were well fitted globally and locally. The CO and NO concentrations were negatively correlated with the mean topographic elevation and NO concentrations were correlated with building density.
Graphical Abstract
... In recent years, although many scholars have made in-depth research on the causes of smog [5,6], prediction [7−9], prevention and control [10−12] and other aspects, making the smog control effect significant, it still has a long way to go. From the aspect of smog simulation and prediction, many scholars have mastered many effective methods, such as coupled four-dimensional local ensemble transform Kalman filter (4D-LETKF) and weather research and forecasting model coupled with chemistry (WRF-Chem) system [13], autoregressive integrated moving average model (ARIMA) [14], nonparametric panel model [15] and so on. All the above models provide theoretical support for the forecast of smog weather. ...
In this paper, an optimization model is proposed to simulate and predict the current situation of smog. The model takes the interval grey number sequence with the known possibility function as the original data, and constructs a time-delay nonlinear multivariable grey model
based on the new kernel and degree of greyness sequences considering its time-delay and nonlinearity. The time-delay parameter is determined by the maximum value of the grey time-delay absolute correlation degree, and the nonlinear parameter is determined by the minimum value of average relative error. In order to verify the feasibility of the model, this paper uses the smog related data of Nanjing city for simulation and prediction. Compared with the other four models, the new model has higher simulation and prediction accuracy.
... Third, it was found that there is an inverted "U" relationship between population density and air pollution. This showed that the crowding effect of population aggregation precedes the scale effect of population aggregation (Liu et al., 2021). When the urban economic level is low, resources are not fully utilized, and the economic growth pattern is extensive (within a certain range), the increase in population, the increase in land development intensity and the need for industrial development bring more serious pollution at first. ...
For a long time, air pollution caused by unreasonable urban spatial structure and excessive urban sprawl has been a prominent environmental problem in China. From the level of all cities, three economic zones and different city scales, panel data of 194 prefecture-level cities in China from 2006 to 2017 were used to construct a dynamic panel model and to analyze the impact of urban spatial structure on SO2, industrial smoke and dust emissions. The results showed that: (1) air pollution had a time cumulative effect year by year, the air pollution of the last year could add air pollution in the script year; (2) urban space expansion could effectively curb air pollution; (3) the urban spatial structure with high population compactness made the air pollution change in an inverted "U" shape; (4) in different economic zone levels and different urban scale levels, the direction of influence and intensity of urban spatial structure on air pollution was different. In the eastern region of China, the residential land, public facilities land and traffic land in the urban structure mainly affected the air pollution. In the central region, the residential land, industrial land, traffic land and municipal land in the urban structure had a significant impact on the air pollution, while the urban scale was the main cause of the air pollution in the western region. Based on this, we recommended the reasonable planning of land use structure, establishment of a population density regulation mechanism, and paying attention to regional differences and urban size differences. This study can help managers of different economic zones and cities of different sizes to improve urban spatial structure and control air pollution in the process of urban development.
As China promotes green and high-quality urban development,
the urban polycentric spatial structure
(PSS) has become an important means for cities to achieve environmental sustainability. There have been extensive studies on the impact of PSS on
haze pollution
(HP) from various views, but few studies have been done on how HP affects PSS. Thus, this letter aims to explore the impact of HP on PSS. To achieve this, the urban polycentric indexes were developed at first from the remote sensing nighttime light perspective. Then, the econometric models were employed to examine how HP affects PSS in China’s 206 prefecture-level cities based on a panel data. Results show that HP promotes urban development towards PSS significantly. Moreover, this effect is moderated by the total urban population. For cities with the total urban population lower than 5.777 million, HP is more conducive to promoting PSS. Reasonable measures should be taken to alleviate HP, and the government should balance the development of various regions to achieve high-quality and sustainable development in polycentric cities.
Strengthening the construction of ecological civilization is an inevitable requirement for promoting high-quality economic and social development. It is of great significance to study the evolutionary trend and relationship between urban spatial structure and ecological efficiency to promote high-quality social development. Taking Shandong Province as an example, this paper obtains data on urban factors such as points of interest, night light, number of employed people at the end of the year and water supply; uses Anselin Local Moran’s I index to identify urban centers; analyzes the distribution form characteristics and agglomeration degree of urban space; and studies the spatial distribution characteristics and causes of differences in ecological efficiency based on the Super-SBM DEA model with undesirable output. The results show that all cities in Shandong Province show an inverse S-shaped circle decreasing trend, Laiwu city has the highest compactness (compactness index is 2.96), and Tai ‘an city has the lowest compactness index of 0.04. The level of eco-efficiency in Shandong Province is “low in the west and high in the east”, and the difference in eco-efficiency between regions is increasing year by year. Urban compactness has a “first increasing and then decreasing” effect on eco-efficiency. Technological innovation and industrial structure narrow the spatial difference in eco-efficiency, and the level of economic development expands it to a certain extent. This study aims to fill the gaps in existing research. By analyzing data on the evolution of urban spatial structure and resource consumption, it will reveal the trends of changes in the urban spatial structure of Shandong Province and study the impact of these changes on ecological benefits.
Urban spatial structure has important impact on environmental pollutant emissions, but few studies have investigated the possibility of a bidirectional influence mechanism between the two. This study used panel data of 108 prefecture-level cities in the Yangtze River Economic Belt (China) from 2009 to 2018. The objective was to test empirically the dynamic emission reduction effect of polycentric spatial structure on environmental pollution using the STIRPAT model as a framework to decipher the bidirectional causality effect. System generalized method of moments and the Sobel Test were applied to construct a panel intermediary effect model to verify the mechanism of urban polycentric development mode on urban haze pollution in China. Results showed that the urban polycentric structure in the Yangtze River Economic Belt has produced substantial pollution reduction effects, and that there are heterogenous regional, size, and functional differences in the impact of urban spatial structure of the Yangtze River Economic Belt on the environment. Moreover, the results of the intermediary effect test showed that market integration and industrial structure optimization are important ways via which to improve environmental pollutant emissions using the polycentric spatial development model. On this basis, suggestions were proposed for optimizing urban spatial structure to promote high-quality development of the Yangtze River Economic Belt.
To alleviate the ecological and environmental problems caused by rapid urban development, China has formulated and implemented the new-type urbanization strategy. However, there is insufficient empirical research on the specific relationship between new-type urbanization and air pollution. Therefore, based on the panel data of 30 provinces in China from 2005 to 2018, this paper constructs a comprehensive evaluation index system of new-type urbanization using the five dimensions of population, economy, space, society, and green. The spatial Durbin model and the spatial mediating model are used to discuss the spatial effect, transmission mechanism, and regional heterogeneity of new-type urbanization on air pollution. The results show that China’s air pollution mainly presents a spatial pattern of high-high agglomeration and low-low agglomeration, and there are spatial fluctuations. The construction of new-type urbanization significantly reduces local air pollution, and the industrial structure optimization, technological innovation, and energy structure adjustment are considered as important transmission mechanisms. However, under the fiscal decentralization and political tournament system in China, the policy implementation deviation may weaken the emission reduction effect of new-type urbanization, which is not conducive to regional environmental governance. From the sub-regional level, the impact of new-type urbanization on air pollution has regional heterogeneity. A robustness test confirms the reliability of our research conclusions. This study also proposes some policy suggestions that the government can utilize in grasping the policy focus of new-type urbanization construction to discover effective ways of controlling air pollution.
This paper employs the slack-based measure method and an extended Luenberger productivity indicator to estimate and decompose the atmospheric environmental performance under the constraints of energy and atmospheric pollutant emissions [i.e., the growth of the atmospheric environment total factor productivity (AETFP)] of the “three regions and ten urban agglomerations” (TRTAs) in China. Specifically, undesirable output is considered as both carbon and air pollutant emissions, i.e., CO2, SO2, and NOx emissions. Also, based on the proposed approach, we identify the different paths of the technical change as a crucial driver of the AETFP growth. Furthermore, using the spatial econometric model with a symmetric geographical distance weight matrix and an asymmetric economic geography weight matrix, we investigate the effect of different types of environmental regulation on the AETFP growth to verify the Porter hypothesis in China. The results show that the main drivers of China’s atmospheric environment inefficiency are air pollutant emissions (SO2 and NOx), carbon emissions, and fossil energy use. Spatially, the environment inefficiency presents a decreasing trend from northern China to southern China. The improved performance of SO2 emissions made more contributions to the AETFP growth during China’s 11th “Five-Year Plan” period (2006–2010), while NOx emissions has a marginal positive effect on the AETFP growth is marginal. Despite the differences in the technical change across regions, the technical progress offsets the negative impact of declining technical efficiency on the AETFP growth. Overall, energy-saving and emission-reduction policies and technologies in TRTAs exert a decisive influence on the AETFP growth. In particular, the spatial econometric results indicate that the market-motivated environmental regulation has a positive effect on the AETFP growth and thus conforms to the Porter hypothesis in China but does not cause the “race-to-the-bottom” effect among local governments, while the command-and-control oriented regulation leads to a “race-to-the-bottom” effect and undermines the AETFP growth.
We examine the effect of research and development (R&D) intensity on carbon dioxide (CO2) emissions in the Group of Seven (G7) countries since the nineteenth century using a non-parametric panel data model. Our estimates suggest that the relationship between R&D and CO2 emissions is time-varying. The estimated time-varying coefficient function of R&D was negative for three quarters of the period studied, but was positive for a 35-year period (1955-1990) during the second half of the twentieth century. Our non-parametric local linear estimates show that the common trend functions gradually increased for the first 110 years (1870-1980), but then flattened out and showed a slight decrease for the next three decades.
As part of the Paris Agreement, China pledged to peak its CO2 emissions by 2030. In retrospect, the commitment may have been fulfilled as it was being made—China’s emissions peaked in 2013 at a level of 9.53 gigatons of CO2, and have declined in each year from 2014 to 2016. However, the prospect of maintaining the continuance of these reductions depends on the relative contributions of different changes in China. Here, we quantitatively evaluate the drivers of the peak and decline of China’s CO2 emissions between 2007 and 2016 using the latest available energy, economic and industry data. We find that slowing economic growth in China has made it easier to reduce emissions. Nevertheless, the decline is largely associated with changes in industrial structure and a decline in the share of coal used for energy. Decreasing energy intensity (energy per unit gross domestic product) and emissions intensity (emissions per unit energy) also contributed to the decline. Based on an econometric (cumulative sum) test, we confirm that there is a clear structural break in China’s emission pattern around 2015. We conclude that the decline of Chinese emissions is structural and is likely to be sustained if the nascent industrial and energy system transitions continue.
We examine the relationship between oil prices and economic policy uncertainty in G7 countries. To do so, we employ a nonparametric panel data technique that allows the trend and coefficient functions to evolve as unknown time-varying functional forms. We also estimate country-specific and common trend functions allowing them to evolve
over time. Using monthly data from G7 countries over the period 1997:01-2018:06, we find that the effect of oil prices on economic policy uncertainty is time-varying. Our results show that the estimated time-varying coefficient function of the oil price was negative in years in which increases in oil prices were driven by a surge in global aggregate demand. Further, our nonparametric local linear estimates show that the country specific and common trend functions are increasing over time. Our findings are robust to endogeneity and alternative specifications.
This paper introduces an innovative nonparametric panel data approach to model the long- run relationship between the monthly oil price index and stock market price indices of ten large net oil importing countries; namely, the United States, Japan, China, South Korea, India, Germany, France, Singapore, Italy and Spain. In the proposed model, we allow the coefficient on the oil price index to be a time-varying function which evolves over time in a way that is assumed to be unknown. We also allow the common trend function to evolve over time, as well as extending the model further to incorporate country-specific trend functions. We employ a data-driven local linear method to estimate these time-varying trend and coefficient functions. The results show that, despite being largely positive, there are several downward trends, reflecting the aftermath of the Iraq war and the recent unprecedented drop in the oil price. Overall, we find that the nonparametric panel data model better captures the way in which the underlying stock-oil price relationship has evolved over time in comparison to the point estimates of the parametric counterpart. Moreover, we find that stock market fundamentals play a significant role in determining the oil-stock price relationship. Our findings have important implication for policymakers and financial speculators.
The main purpose of this study is to evaluate the relevancy of environmental Kuznets curve (EKC) hypothesis to the environment problem of today world. According to EKC hypothesis, continuous economic growth eventually reverses the environmental degradation created at the early stage of economic development. This hypothesis emerged in the 1990s and led many serious commentators of economic development to assume that developing countries should focus on economic growth and any environmental problem would be automatically solved by the process of economic growth. The necessary message of EKC was “grow now clean later.” The empirical studies on EKC lead to the conclusion that EKC transition exists only for local pollutants. We found that EKC empirical literature is not econometrically sound and the relationship of many types of pollutants with income has not been tested yet due to the non-availability of data. We also conclude that EKC transition is not Pareto efficient and EKC growth strategy is resource intensive and has huge environmental cost that this planet may not be able to absorb in future. The key recommendations of the study are that developing world should follow different growth path than that of EKC. They should choose a growth path that is not detrimental to the environment so that stock of pollution created by advanced countries can be contained and advanced countries should make green technologies affordable to developing countries.
The theories of ecological modernization and urban environmental transition both recognize that urbanization can have positive and negative impacts on the natural environment with the net effect being hard to determine a priori. This study uses recently developed panel regression techniques that allow for heterogeneous slope coefficients and cross-section dependence to model the impact that urbanization has on CO2 emissions for a panel of emerging economies. The estimated contemporaneous coefficients on the energy intensity and affluence variables are positive, statistically significant and fairly similar across different estimation techniques. By comparison, the estimated contemporaneous coefficient on the urbanization variable is sensitive to the estimation technique. In most specifications, the estimated coefficient on the urbanization variable is positive but statistically insignificant. The implications of these results for sustainable development policy are discussed.
This paper deals with the key role of ecological modernization in bringing about sustainable development. So far, two strategies of sustainable development have been discussed: sufficiency and efficiency. Sufficiency is preferred by the organized ecology movement (non-governmental organizations (NGOs)), meaning self-limitation of material needs, withdrawal from the free world-market economy and an egalitarian distribution of the remaining scarce resources. Contrary to that, industry and business have adopted the ‘efficiency revolution’ as a strategy to allow further economic growth and ecological adaptation of industrial production by improving the environmental performance, i.e. improving the efficient use of material and energy, thus increasing resource productivity in addition to labour and capital productivity. There are good reasons for both sufficiency and efficiency. Nevertheless, they do have important shortcomings. An additional, third kind of transformational strategy needs to be pursued. In the present name-giving context, one could call it the strategy of ‘consistency’. A term with a similar meaning in the current discussion is ‘industrial ecology’. Industrial ecology aims at an industrial metabolism that is consistent with nature's metabolism. The transformation of traditional industrial structures, which are often environmentally unadapted to an ecologically modernized consistent industrial metabolism, implies major or basic technological innovations, as being different from incremental efficiency increasing change. Copyright © 2000 John Wiley & Sons, Ltd.
We consider the problem of estimating a varying coefficient panel data model with fixed-effects (FE) using a local linear regression approach. Unlike first-differenced estimator, our proposed estimator removes FE using kernel-based weights. This results a one-step estimator without using the backfitting technique. The computed estimator is shown to be asymptotically normally distributed. A modified least-squared cross-validatory method is used to select the optimal bandwidth automatically. Moreover, we propose a test statistic for testing the null hypothesis of a random-effects varying coefficient panel data model against an FE one. Monte Carlo simulations show that our proposed estimator and test statistic have satisfactory finite sample performance.
We develop a statistical framework to use satellite data on night lights to augment official income growth measures. For countries with poor national income accounts, the optimal estimate of growth is a composite with roughly equal weights on conventionally measured growth and growth predicted from lights. Our estimates differ from official data by up to three percentage points annually. Using lights, empirical analyses of growth need no longer use countries as the unit of analysis; we can measure growth for sub- and supranational regions. We show, for example, that coastal areas in sub-Saharan Africa are growing slower than the hinterland. (JEL E01, E23, O11, 047, 057)
A global poverty map has been produced at 30 arcsec resolution using a poverty index calculated by dividing population count (LandScan 2004) by the brightness of satellite observed lighting (DMSP nighttime lights). Inputs to the LandScan product include satellite-derived land cover and topography, plus human settlement outlines derived from high-resolution imagery. The poverty estimates have been calibrated using national level poverty data from the World Development Indicators (WDI) 2006 edition. The total estimate of the numbers of individuals living in poverty is 2.2 billion, slightly under the WDI estimate of 2.6 billion. We have demonstrated a new class of poverty map that should improve over time through the inclusion of new reference data for calibration of poverty estimates and as improvements are made in the satellite observation of human activities related to economic activity and technology access.
Recent concepts such as ‘megaregions’ and ‘polycentric urban regions’ emphasize that external economies are not confined to a single urban core, but are shared among a collection of nearby and linked cities. However, empirical analyses of agglomeration and agglomeration externalities have so far neglected the multicentric spatial organization of agglomeration and the possibility of the ‘sharing’ or ‘borrowing’ of size between cities. The authors take up this empirical challenge by analyzing how different spatial structures, in particular the monocentricity – polycentricity dimension, affect the economic performance of US metropolitan areas. Ordinary least squares and two-stage least-squares models explaining labor productivity show that spatial structure matters: polycentricity is associated with higher labor productivity. This appears to justify suggestions that, compared with more monocentric metropolitan areas, agglomeration diseconomies remain relatively limited in the more polycentric metropolitan areas, whereas agglomeration externalities are to some extent shared among the cities in such an area. However, it was also found that a network of geographically proximate smaller cities cannot substitute for the urbanization externalities of a single large city.
Urban structure is increasingly characterized by decentralization, dispersion, and multiple employment centers. Much is known empirically about such patterns, and about how the interplay between agglomerative and dispersive forces generates spatial structures that are complex and prone to multiple equilibria and dynamic path-dependence. These forces operate at different spatial scales; many entail unpriced interaction, and external scale economies deriving from product differentiation and endogenous technical change appear particularly important. Because these forces interact in complex ways, inefficiencies in urban structure are resistant to simple policy interventions.
The purpose of this study is to examine the validity of Environmental Kuznets Curve hypothesis for ecological footprint with the role of renewable energy use, non-renewable energy use and trade openness in 24 Organisation for Economic Co-operation and Development countries. For this purpose, we investigate the period from 1980 to 2014 using with second generation panel data methodologies which allow to cross-sectional dependence among countries. The group-mean results show that the inverted U-shaped Environmental Kuznets Curve hypothesis does not hold in Organisation for Economic Co-operation and Development countries because we found the U-shaped relationship between economic growth and ecological footprint. In addition, it is concluded that increasing renewable energy consumption reduces ecological footprint and increasing non-renewable energy consumption increases environmental degradation.
In this study, the provincial panel data of 29 provinces in China from 2003 to 2016 were selected to conduct an empirical study on the impact of China's outward foreign direct investment (OFDI) on domestic carbon dioxide emissions. The three major effects of OFDI on environmental pollution in the home country, namely scale effect, technical effect and composition effect, are estimated by a single equation model and simultaneous equation model. The results show that China's OFDI has increased domestic environmental pollution by improving economic scale (scale effect). However, the reverse technology spillover effect brought by OFDI has improved the domestic technology level (technical effect) and optimized the domestic industrial structure (composition effect), thereby reducing domestic environmental pollution. Based on the research results, this paper puts forward relevant policy suggestions for the government.
The improvement in energy consumption structure is of great significance to the green transformation of economic development. In this paper, to explore the reasonable improvement pathway of energy consumption structure in China's industrial sector, we treat fossil energy and non-fossil energy as two different factors into the production function, and conduct a stochastic frontier analysis to estimate the factor-biased degree of production technical change and the substitution elasticities between factors. The results show that the production technology of China's industrial sector is more biased to fossil energy and labor and deviated from non-fossil energy and capital. There is a substitution relationship between capital and labor, as well as labor and fossil energy, and the relationship between capital and fossil energy is complementary. We find that the energy consumption structure in most industrial sub-sectors has a large room for improvement. We propose that the Chinese government should promote the market-oriented reform of energy pricing mechanism to improve the energy consumption structure based on the differentiated characteristics of the energy consumption structures of different industrial sub-sectors.
Emissions from heavy urban traffic have been the most abundant components of urban air pollution in China. Compared to the fast increasing consumption of automobile, the growth rate of urban traffic infrastructure investment (UTII) is relative low. It is a controversial issue for the policymaker whether promoting the UTII could make a positive impact on reducing air pollution or not. This study is designed to test the relations between the UTII and air quality, using the data of 83 cities from 2000 to 2012. Based on the empirical results from the fixed effect model and dynamic panel data model, increasing the UTII could generally mitigate the air pollution, but the long-run and short-run effects are significantly different. In the short run, the UTII effect on air quality is −0.02, since the urban traffic infrastructure construction might cause more detours and road blockages, which would enhance the fine-particle emissions of the low-speed traffic. In contrast, the UTII could widen the roads and make the traffic system more accessible in the long run, so the estimated positive effect is about 0.05. The robustness tests about different regions and scales of cities are conducted. Policy suggestions are further recommended to reduce air pollution.
The main contribution of this study is to test the Environmental Kuznets Curve (EKC) hypothesis at individual country analysis by the significance of renewable energy and non-renewable energy consumption in the context of Pakistan. A series of econometric techniques is used for the period of 1970-2012. The findings provide strong support for the presence of the EKC in the case of Pakistan. The results show that renewable energy plays a dominant role in reducing carbon dioxide emission and non-renewable energy consumption indeed the main culprit for promoting carbon dioxide emission. It is also observed that bi-directional causality exists not only between renewable energy consumption and CO2 emission but also non-renewable energy consumption and CO2 emission granger cause each other. Sensitivity analysis is performed to prove that model of this study is stable and findings of the study are valid and reliable for Policy implication. These findings suggest that Government needs to expand investment in renewable energy projects that might contribute to the efforts of climate mitigation.
Carbon dioxide emissions may create significant social harm because of global warming, yet American urban development tends to be in low density areas with very hot summers. In this paper, we attempt to quantify the carbon dioxide emissions associated with new construction in different locations across the country. We look at emissions from driving, public transit, home heating, and household electricity usage. We find that the lowest emissions areas are generally in California and that the highest emissions areas are in Texas and Oklahoma. There is a strong negative association between emissions and land use regulations. By restricting new development, the cleanest areas of the country would seem to be pushing new development towards places with higher emissions. Cities generally have significantly lower emissions than suburban areas, and the city-suburb gap is particularly large in older areas, like New York.
This study combines a panel cointegration analysis with a set of robustness tests to assess the short and long-run impacts of renewable energy on CO2 emissions, as well as the Kuznets Environmental Curve hypothesis for 25 selected african countries, over the period 1980-2012. The results provide no evidence of a total validation of EKC predictions. However, CO2 emissions are found to increase with income per capita. The overall estimations strongly reveal that renewable energy, with a negative effect on CO2 emissions, coupled with an increasing long-run effect, remains an efficient substitute for the conventional fossil-fuelled energy. Nonetheless, the impact of renewable energy is outweighed by primary energy consumption in both the short and long run, entailing more global synergy for outpacing the environmental challenges.
We examine the reduced-form relationship between per capita income and various environmental indicators. Our study covers
four types of indicators: urban air pollution, the state of the oxygen regime in river basins, fecal contamination of river
basins, and contamination of river basins by heavy metals. We find no evidence that environmental quality deteriorates steadily
with economic growth. Rather, for most indicators, economic growth brings an initial phase of deterioration followed by a
subsequent phase of improvement. The turning points for the different pollutants vary, but in most cases they come before
a country reaches a per capita income of $8000.
There is a rich literature on nonparametric and semiparametric estimations of panel or longitudinal data. However, few studies focus on the nonparametric and semiparametric panel models with fixed effects. We consider semiparametric partially linear varying-coefficient models for panel data with fixed effects. We propose consistent estimators for both the parametric and nonparametric components in the models based on profile likelihood and local linear smoothing method. Simulation shows that our estimators behave quite well.
The aim of the research described in this paper is to examine the validity of the claims that higher-density urban form promotes social equity—that is, promotes benefits for the life-chances of low-income groups. Overall, the evidence suggests that, for medium-sized English cities, higher urban densities may be positive for some aspects of social equity and negative for others. More specifically, likely benefits include improved public transport, reduced social segregation and better access to facilities, while the main problems are likely to be reduced living space and a lack of affordable housing. Investigations of different aspects of density show that the cities that most support social equity appear to be those that have a large proportion of high-density housing.
Summary This paper proposes a test statistic for the null hypothesis of panel stationarity that allows for the presence of multiple structural breaks. Two different specifications are considered depending on the structural breaks affecting the individual effects and/or the time trend. The model is flexible enough to allow the number of breaks and their position to differ across individuals. The test is shown to have a standard normal limit distribution with a good finite sample performance. It is applied to typical panel data of real per capita GDP in a set of OECD countries.
Carbon dioxide emissions may create significant social harm because of global warming, yet American urban development tends to be in low density areas with very hot summers. In this paper, we attempt to quantify the carbon dioxide emissions associated with new construction in different locations across the country. We look at emissions from driving, public transit, home heating, and household electricity usage. We find that the lowest emissions areas are generally in California and that the highest emissions areas are in Texas and Oklahoma. There is a strong negative association between emissions and land use regulations. By restricting new development, the cleanest areas of the country would seem to be pushing new development towards places with higher emissions. Cities generally have significantly lower emissions than suburban areas, and the city-suburb gap is particularly large in older areas, like New York.
Empirical studies of the Environmental Kuznets Curve (EKC) examine the presence or otherwise of an inverted U-shaped relationship between the level of pollution and the level of income. Customarily, in the diagram of EKC the level of income is shown on the horizontal axis and that of pollution on the vertical axis. Thus, it is presumed that the relationship between income and pollution is one of unidirectional causality with income causing environmental changes and not vice versa. The validity of this presumption is now being questioned. It is being asserted that the nature and direction of causality may vary from one country to the other. In this paper, we present the results of a study of income–CO2 emission causality based on a Granger causality test to cross-country panel data on per capita income and the corresponding per capita CO2 emission data. Briefly, our results indicate three different types of causality relationship holding for different country groups. For the developed country groups of North America and Western Europe (and also for Eastern Europe) the causality is found to run from emission to income. For the country groups of Central and South America, Oceania and Japan causality from income to emission is obtained. Finally, for the country groups of Asia and Africa the causality is found to be bi-directional. The regression equations estimated as part of the Granger causality test further suggest that for the country groups of North America and Western Europe the growth rate of emission has become stationary around a zero mean, and a shock in the growth rate of emission tends to generate a corresponding shock in the growth rate of income. In contrast, for the country groups of Central and South America, Oceania and Japan a shock in the income growth rate is likely to result in a corresponding shock in the growth rate of emission. Finally, causality being bi-directional for the country groups of Asia and Africa, the income and the emission growth rates seemed to reinforce each other.
This paper investigates the relationship between environmental pollution and economic growth in China based on the environmental Kuznets curve hypothesis, using Chinese provincial data over 1985–2005. Waste gas, waste water and solid wastes are used as environmental indicators and GDP is used as the economic indicator. It is found by panel cointegration test that there is a long-run cointegrating relationship between the per capita emission of three pollutants and the per capita GDP. According to comparisons with the dynamic OLS estimator and the Within OLS estimator, we find that panel cointegration estimation is preferable for all pollutants except for solid wastes. The results also show that all three pollutants are inverse U-shaped, and water pollution has been improved earlier than gas pollution and solid pollution.
This paper employs a conditional logit model to estimate the effects of state environmental regulations on foreign multinational corporations' new plant location decisions from 1986 to 1993. The relationship between site choice and state environmental regulations is explored, using four measures of regulatory stringency. We find evidence that heterogeneous environmental policies across states do matter.
Using recent theories of growth, this paper presents empirical evidence that export diversification promotes economic growth. This result is robust to different specifications of the growth equations and different measures of export diversification. In developing countries, export diversification is associated also with higher investment rates.
Cities can be thought of as the absence of physical space between people and firms. As such, they exist to eliminate transportation costs for goods, people and ideas and transportation technologies dictate urban form. In the 21st century, the dominant form of city living is based on the automobile and this form is sometimes called sprawl. In this essay, we document that sprawl is ubiquitous and that it is continuing to expand. Using a variety of evidence, we argue that sprawl is not the result of explicit government policies or bad urban planning, but rather the inexorable product of car-based living. Sprawl has been associated with significant improvements in quality of living, and the environmental impacts of sprawl have been offset by technological change. Finally, we suggest that the primary social problem associated with sprawl is the fact that some people are left behind because they do not earn enough to afford the cars that this form of living requires.
This paper models and estimates net urban agglomeration economies for cities. Economic models of cities postulate an inverted
U shape of real income per worker against city employment, where the inverted U shifts with industrial composition across
the urban hierarchy of cities. This relationship has never been estimated, in part because of data requirements. China has
the necessary data and context. We find that urban agglomeration benefits are high—real incomes per worker rise sharply with
increases in city size from a low level. They level out nearer the peak and then decline very slowly past the peak. We find
that a large fraction of cities in China are undersized due to nationally imposed, strong migration restrictions, resulting
in large income losses.
This paper develops a very simple test for the null hypothesis of no cointegration in panel data. The test is general enough to allow for heteroskedastic and serially correlated errors, unit-specific time trends, cross-sectional dependence and unknown structural breaks in both the intercept and slope of the cointegrated regression, which may be located at different dates for different units. The limiting distribution of the test is derived, and is found to be normal and free of nuisance parameters under the null. A small simulation study is also conducted to investigate the small-sample properties of the test. In our empirical application, we provide new evidence concerning the purchasing power parity hypothesis. Copyright (c) Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2008.
This paper proposes a residual-based Lagrange multiplier (LM) test for a null that the individual observed series are stationary around a deterministic level or around a deterministic trend against the alternative of a unit root in panel data. The tests which are asymptotically similar under the null, belong to the locally best invariant (LBI) test statistics. The asymptotic distributions of the statistics are derived under the null and are shown to be normally distributed. Finite sample sizes and powers are considered in a Monte Carlo experiment. The empirical sizes of the tests are close to the true size even in small samples. The testing procedure is easy to apply, including, to panel data models with fixed effects, individual deterministic trends and heterogeneous errors across cross-sections. It is also shown how to apply the tests to the more general case of serially correlated disturbance terms.
Considerable interest has focused on the possible existence of an environmental Kuznets curve, whereby pollution first increases but later falls with increasing income. Empirical studies have concentrated on a wide spectrum of countries and run into inevitable problems of data comparability and quality. We avoid these problems by looking at seven types of air emissions across the 50 US states and find all seven pollutants decrease with increasing per capita income. We also find strong evidence of heteroscedasticity with respect to the income emissions relationship: lower-income states display much greater variability in per capita emission levels than higher-income states. Additionally, we look at the best measured of these emissions, air toxics, for the period 1988 94. Using a simple sign test, we find support for the notion that an increase in income is associated with a decrease in per capita emissions. However, the change in emissions appears to be unrelated to the magnitude of the change in income. We do find, though, that the reduction in per capita emissions is increasing both in terms of the 1988 level of per capita emissions and income. Possible implications of these results for the development process are discussed.
This paper examines the relationship between per capita income and a wide range of environmental indicators using cross-country panel sets. The manner in which this has been done overcomes several of the weaknesses asscociated with the estimation of environmental Kuznets curves (EKCs). outlined by Stern et al. (1996). Results suggest that meaningful EKCs exist only for local air pollutants whilst indicators with a more global, or indirect, impact either increase monotonically with income or else have predicted turning points at high per capita income levels with large standard errors unless they have been subjected to a multilateral policy initiative. Two other findings are also made: that concentration of local pollutants in urban areas peak at a lower per capita income level than total emissions per capita; and that transport-generated local air pollutants peak at a higher per capita income level than total emissions per capita. Given these findings, suggestions are made regarding the necessary future direction of environmental policy.
Urbanization promotion and smog pollution governance in China
- Shao
Urban spatial structure and regional economic efficiency: a concurrent discussion on the choice of urbanization development in China
- Liu
Does urban sprawl accelerate smog pollution: an empirical study of fine particles (pm2.5) in Chinese cities
- Qin
The impact of urban sprawl on regional economic growth: empirical researches based on DMSP night-time light data
- Qin
China’s economic policy choices for governing smog pollution based on spatial spillover effects
- Shao
Does environmental regulation cause pollution to transfer nearby?
- Shen
Does FDI cause environmental pollution in China?
- Xu