Search

Due to the rapid progress of urbanization in China, the percentage of residential energy consumption out of total energy consumption has increased. This paper uses statistical data from 30 Chinese provinces (autonomous regions and municipalities) from 2000 to 2020 to analyze the impact of urbanization on residential energy consumption and construct an econometric model to test the mechanism. The empirical tests show that the consumption of direct energy (energy that exists in nature in its original form and has not been transformed) is positively U-shaped about the urbanization rate. Furthermore, the impact of economic development on direct and indirect energy consumption is significantly positive. In contrast, the effects of population agglomeration on immediate energy consumption are adverse, and the indirect energy consumption is positive. [ABSTRACT FROM AUTHOR]

This paper has used a stirpat model to investigate the synergistic effect of CO2 emission, energy consumption, energy intensity, economic growth, population, urbanization and trade openness to demonstrate growthenvironment nexus in four selected developing Asian economies. Taking a panel data set from Bangladesh, China, India and Indonesia this study applies Autoregressive Distributed Lag (ardl) model and vec Granger Causality/Block Exogeneity Wald Tests. The empirical results show that energy intensity, urbanization, population, and per capita gdp growth are the raison d'être of CO2 emissions whereas trade openness is found to be negatively related to CO2 emissions. Conversely, energy consumption, urbanization, population and trade openness are positively related to per capita gdp. In addition, it also investigates the Environmental Kuznets Curve (EKC) hypothesis and the findings substantiate an inverted Ushaped relationship. Cross-section short-run coefficients of country-level data are inquired into to check the robustness of the panel outcomes. [ABSTRACT FROM AUTHOR]

Traditional energy planning methods are mostly related to newly-built area. However, in most already-built regions, the existing district energy systems (DESs) need to be ameliorated due to insufficient capacity and low efficiency. This paper investigates the methodology of expansion planning, which can offer planners a viable way to expand existing DESs. The term “expansion” aims to increase the supply capacity of the energy forms, e.g., steam, hot water and electricity, which cannot be accessed from national or urban energy supply network, and meanwhile increase energy utilization efficiency and cut CO 2 emission. Three parts of the methodology, including problem analysis in current DESs, demand side analysis and the existing DES expansion, are introduced in this paper. New concepts of load structure and evaluation system of the whole district are proposed in this methodology. A case analysis of Ningbo Hi-Tech District, China is conducted to shed light on the application of this method. [ABSTRACT FROM AUTHOR]

The rapid urbanization in China has been associated with a growing hunger for energy consumption and steadily-increasing CO2 emissions. In this paper, an integrated system dynamics model composed of four sub-models is developed to simulate the urbanization and energy consumption in China from 1998 to 2050. Three scenarios are provided: accelerated economic development, emission reduction constraint, and low-carbon oriented. The result reveals that rapid economic growth and sufficient energy supply will foster China's urbanization in all three scenarios. Under the low carbon transition scenario, China's urbanization rate is expected to reach 76.41% in 2050, both reducing carbon emissions and promoting eco-friendly development. All three scenarios witness a dramatic growth of residential energy consumption and a steady increase of industrial energy consumption. China still has a long way to achieve the low-carbon transition goal. China should promote renewable resources and energy, pursue a low-carbon lifestyle, and reduce energy intensity over the next few decades. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of urbanization on provincial energy efficiency in China. Using panel data of 30 Chinese provinces between 2003 and 2014, we adopt a stochastic frontier model to estimate energy efficiency in China, and apply a series of spatial panel data models to tackle spatial dependence of energy efficiency between provinces. Our results show that the overall impact of urbanization on energy efficiency is negative in China. These results are established after controlling for spatial correlation and other confounding factors, and are also robust to various model specifications. We also document that the impact of urbanization on provincial energy efficiency manifests through both a direct negative impact induced by the focal province's urbanization process, and an indirect positive spillover effect caused by urbanization in adjacent areas. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization has had a dramatic impact on the ecological environment in China. In this paper, we first measure the urban eco-efficiency of 281 prefecture-level cities in China during 2006–2013 using the super-efficiency data envelopment analysis (SEDEA) model. We then propose a new comprehensive index system to assess urbanization. Finally, we quantitatively explore the relationship between urbanization and urban eco-efficiency based on a spatial econometric analysis. The results showed that China's average urban eco-efficiency and comprehensive level of urbanization of 281 prefecture-level cities had shown increase during 2006–2013. However, distinct spatial disparities exist among different cities. China's urbanization has significant characteristics of spatial agglomeration and spatial convergence during 2006–2013, with a high-to-low spatial distribution from eastern China to western China. Spatial effects play an important role in the changes of urban eco-efficiency in China through spatial spillover effects and regional replication and competition. Evidence from spatial econometric analysis reveals that an N-shaped relationship exists between urbanization and urban eco-efficiency, indicating that with the improvement of urbanization, urban eco-efficiency first increases, then declines and then increases again. The improvement of the level of technology and the formulation of effective environmental policies will promote the increase in urban eco-efficiency. The results provide significant references for urban ecological management and sustainable urban transformation in China. [ABSTRACT FROM AUTHOR]

The surge in energy consumption and CO 2 (carbon dioxide) emissions is exacerbating global warming. The Chinese government has promised to reduce CO 2 emission by 60%–65% per unit of GDP over the 2005 level by around 2030. Analyzing the factors influencing CO 2 emissions is one of the key aspects involved in effectively reducing greenhouse gas emissions. In this paper, we add climatic factors based on socio-economic factors, and adopt the extended STIRPAT model to investigate the mechanism driving emissions by the co-action of natural and socio-economic factors. The results show that the 1995–2014 emissions in China's Zhejiang province had an upward trend with growth rates varying between intermediate periods. The order of the factors' elasticity coefficients is urbanization rate (0.181) > GDP per capita (0.180) > population (0.177) > energy intensity (0.170) > trade openness (0.170) > cooling degree days (0.127) > mean temperature anomaly (0.116). Economic development and urbanization were the main factors promoting CO 2 emissions in 1996–1999, while economic development and foreign trade were the main factors in 2000–2014. Policy makers should pay more attention to keeping a healthy pace of economic development, introducing more low energy consumption and low pollution enterprises and encouraging technological innovation. Improving the quality of urbanization, guiding a sustainable lifestyle, adjusting the structure of foreign trade and curbing energy intensity are also expected to reduce emissions. [ABSTRACT FROM AUTHOR]

China intends to rely on urbanization policies to increase its economic growth even against the backdrop of new constraints on energy consumption. This paper investigates the impact of urbanization on economic growth and energy consumption using data from China’s 266 prefecture-level cities over the 2000–2010 period and examines the heterogeneity of this impact across income and urbanization groups. The main results of our empirical estimation indicate that: (1) the effect of urbanization on economic growth is positive and significant for the entire sample, although it is positive and significant only for high- and middle-income groups (and not the low-income group); (2) the impact of urbanization on energy consumption is positive and significant for all groups when the sample is divided into three urbanization groups based on the Northam curve, indicating that urbanization will lead to large increases in energy consumption. In summary, our empirical results show that urbanization targets will be difficult to achieve in terms of increasing GDP per capita under energy consumption constraints. [ABSTRACT FROM AUTHOR]

Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO2) emissions are closely related to the scope and intensity of human activities. In this study, we assess the utility of nighttime light data as a powerful tool to reflect CO2 emissions from energy consumption, analyze the uncertainty associated with different nighttime light data for modeling CO2 emissions, and provide guidance and a reference for modeling CO2 emissions based on nighttime light data. In this paper, Mainland China was taken as a case study, and nighttime light datasets (the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light data and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data) as well as a global gridded CO2 emissions dataset (PKU-CO2) were used to perform simple regressions at provincial, prefectural and 0.1° × 0.1° grid levels, respectively. The analyses are aimed at exploring the accuracy and uncertainty of DMSP-OLS and NPP-VIIRS nighttime light data in modeling CO2 emissions at different spatial scales. The improvement of nighttime light index and the potential factors influencing the effects of modeling CO2 emissions based on nighttime light datasets were also explored. The results show that DMSP-OLS is superior to NPP-VIIRS in modeling CO2 emissions at all spatial scales, and the bigger the scale, the more evident the advantages of DMSP-OLS. When modeling CO2 emissions with nighttime light datasets, not only the total amount of lights within a given statistical unit but also the agglomeration degree of lights should be taken into account. Furthermore, the geographical location and socio-economic conditions at the study site, such as gross regional product per capita (GRP per capita), population, and urbanization were shown to have an impact on the regression effect of the nighttime lights-CO2 emissions model. The regression effect was found to be better at higher latitude and longitude areas with higher GRP per capita and higher urbanization, while population showed little effect on the regression effect of the nighttime lights - CO2 emissions model. The limitation of this study is that the thresholds of potential factors are unclear and the quantitative guidance is insufficient. [ABSTRACT FROM AUTHOR]

Rapid urbanization has exerted substantial pressure on China’s energy system and contributed to climate change. To find the key drivers of urban residential energy consumption and CO 2 emissions, this paper uses an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model that employs city-level data to examine the influences of population scale, income level, population compactness and price on house-based residential energy consumption, energy-related CO 2 emissions and private vehicle ownership. The empirical results indicate that factors such as population scale, affluence, and population compactness can lead to increases in residential energy consumption and CO 2 emissions. In terms of transportation, income and population scale positively drive the growth of private vehicle ownership, while the fuel price negatively influences private vehicle ownership. Moreover, population scale is the most important factor in residential energy consumption and CO 2 emissions. Finally, policy recommendations are suggested for China’s urban development strategy and urban design and to encourage technology innovations that reduce residential energy consumption and CO 2 emissions. [ABSTRACT FROM AUTHOR]

As the world's largest developing country, China currently is undergoing rapid urbanization and motorization, which will result in far-reaching impacts on energy and the environment. According to estimates, energy use and carbon emissions in the transportation sector will comprise roughly 30% of total emissions by 2030. Since the late 1990s, transportation-related issues such as energy, consumption, and carbon emissions have become a policy focus in China. To date, most research and policies have centered on vehicle technologies that promote vehicle efficiency and reduced emissions. Limited research exists on the control of greenhouse gases through mode shifts in urban transportation-in particular, through the promotion of public transit. The purpose of this study is to establish a methodology to analyze carbon emissions from the urban transportation sector at the Chinese city level. By using Jinan, the capital of China's Shandong Province, as an example, we have developed an analytical model to simulate energy consumption and carbon emissions based on the number of trips, the transportation mode split, and the trip distance. This model has enabled us to assess the impacts of the transportation mode split on energy consumption and carbon emissions. Furthermore, this paper reviews a set of methods for data collection, estimation, and processing for situations where statistical data are scarce in China. This paper also describes the simulation of three transportation system development scenarios. The results of this study illustrate that if no policy intervention is implemented for the transportation mode split (the business-as-usual (BAU) case), then emissions from Chinese urban transportation systems will quadruple by 2030. However, a dense, mixed land-use pattern, as well as transportation policies that encourage public transportation, would result in the elimination of 1.93 million tons of carbon emissions-approximately 50% of the BAU scenario emissions. [ABSTRACT FROM AUTHOR]

Economic growth, energy consumption, and urbanization are mutually influenced, therefore, forecasting economic growth, energy consumption and urbanization in China has always been important as it could help government to improve energy policies and plans. To this end, two novel grey multivariable models, SRMGM(1, M) and BRMGM(1, m), are designed in this paper. Compared with the MGM(1, M) model, the SRMGM(1, M) model can optimize the background of the traditional model according to the Simpson formula, and the BRMGM(1, m) model optimizes the background value according to a Boolean formula. The reconstruction of background value can reduce the prediction error of MGM(1, M) model. Furthermore, rolling prediction is added to these two new models based on the priority principle of new information. In order to validate their efficacy and accuracy, the two proposed models are employed to reproduce and predict economic growth, energy consumption, and urbanization compared with MGM(1, m) model and three linear regression models. FDs of our models, in both fitted and predicted stages are all over 97%, and the ADFs are all below 5%. The results show that our new models achieves higher fitted and predicted accuracy than MGM(1,m) model and the non-grey models. Eventually, the new models will be used to forecast economic growth, energy consumption, and urbanization from 2020 to 2025, and the forecast results provide a solid basis for economic growth policies and energy consumption plans. [Display omitted] • Two novel grey models are designed and theirs the validity are proved in the paper. • The background value is improved by Simpson formula and Boolean formula. • Two novel models perform best than the non-grey models. • The results improve economic growth policies and energy consumption plans. [ABSTRACT FROM AUTHOR]

Rapid industrial development consumes a large amount of water and energy, which causes both urban water shortages and massive carbon emissions. Therefore, optimization of the urban industrial structure is urgently required to pursue the low-carbon goal under the constraints of water supply. In this paper, we chose Dalian city in China as a case study and divided its industrial sectors into three types according to their output value. Meanwhile, we used the LMDI (Logarithmic Mean Divisia Index) method to decompose the factors that impact industrial carbon emissions from energy consumption. Based on the ratio of the carbon emission effects, five scenarios were set respectively, and accounted for the carbon emissions and water consumption under different scenarios of industrial structure. The results indicated that the economic growth effect was the most significant factor that contributed to industrial carbon emissions, while industrial structure effect was the largest negative factor for increasing industrial carbon emissions. The results also indicated that the total carbon emissions under the HOV (high output-value)-preferred and water-conserved scenario will be lower by 8.5% in 2020 than the baseline scenario, while both its energy intensity and water intensity will meet the requirements of the constraints of the urban development planning. [ABSTRACT FROM AUTHOR]

China is currently experiencing rapid economic growth as a result of industrialization and urbanization. In the next 20 years, China's energy demand will increase substantially, and energy conservation will become imperative for the country's low carbon economic transformation. Since 1998, China's chemical fiber production has ranked first in the world. In 2011, China's chemical fiber production accounted for about 70% of the world's total output. Therefore, energy saving is important for China's chemical fiber industry, and can provide immense benefits, and hence the need for thorough and intense research. In this paper, the co-integration method is used to test the relationship between energy consumption and its influencing factors such as GDP, energy prices, population and R&D expenditures. The results show that there is a long-term relationship between the variables. Based on this, we estimate energy demand and predict the energy-saving potential for the industry under different scenarios. We apply the Monte Carlo simulation method to verify the reliability of the prediction. Scenario analysis is adopted to predict energy demand and energy saving in the industry under different scenarios. Finally, we propose some policy suggestions on energy saving based on the research. [ABSTRACT FROM AUTHOR]

Since the combustion of fossil fuel became the largest contributing factor of greenhouse gas emissions in China, the growth of energy consumption caused by urbanization has led to significant increase in carbon emissions. Applying both the carbon-emissions-coefficient and the sector-energy-consumption method, we predicted the energy consumption and carbon emissions, explored the characteristics of energy consumption and carbon emissions in different urbanization stages, and analyzed the energy crisis in Shandong which is a typical province in China. The results indicate that in 2015 and 2020, the total primary energy consumption will be 1.57 times and 1.85 times higher than that of in 2009, and the carbon emissions are estimated to be 1.48 times and 1.67 times respectively. The urbanization has distinct positive effects on energy consumption and carbon emissions, and the influence is more obvious in the intermediate-stage than in the early-stage. From a policy perspective, this paper could provide valuable information for planners and decision makers to formulate feasible and practical polices with a view to optimize energy structure and mitigate carbon emissions. [ABSTRACT FROM AUTHOR]

China is experiencing a stage of rapid urban development. The energy consumption and related carbon dioxide emissions of households continue to increase. This paper calculates direct and indirect carbon dioxide emissions of households based on the input–output method in China from 1996 to 2012. The results reveal that there were more total carbon dioxide emissions from urban households than from rural households, far more indirect emissions from urban households than from rural households, slightly more direct emissions from urban households than from rural households, and differences in direct carbon dioxide emissions from various fuels and in indirect emissions from various sectors between urban and rural households. To examine the causal relationship between urbanization and the carbon dioxide emissions of households, cointegration and Granger causality tests are applied. A unidirectional causal relation was found running from urbanization to both direct and indirect household carbon dioxide emissions, and the direct and indirect carbon dioxide emissions of households would increase 2.9% and 1.1%, respectively, for every increase of one percent in urbanization. We discuss the reasons of why the development of urbanization will lead to more household direct and indirect carbon dioxide emissions, and suggest certain policy implications for urbanization and carbon dioxide emissions based on the results of this study. [ABSTRACT FROM AUTHOR]

Based on analysis of input–output energy and household expenditure data (IO-EA-expenditure), this paper calculates China’s indirect carbon emissions from residential consumption in 2002 and 2007, by region. Then a new Structural Decomposition Analysis (SDA) model is proposed to investigate the regional variations of impacts of urbanization, consumption ratio and consumption structure on residential indirect CO 2 emissions in China during 2002–2007. As the results suggest, expansion of urbanization and upgrade of consumption structure play important roles in the growth of residential indirect emissions. Transformation of consumption ratio, contributing the most to emissions in the eastern region, reduces indirect emissions in all regions to a certain extent. The persistent decline of carbon emission intensity still contributes significantly to the decrease in emissions and the rise of per capita consumption plays a dominant role in the growth of residential indirect emissions. Based on empirical results, this article provides policy suggestions which can help reduce China’s residential indirect CO 2 emissions. [ABSTRACT FROM AUTHOR]

China is currently experiencing rapid urbanization. The building sector plays an important role in China's energy conservation and emission reduction. An increasing amount of energy will be steadily consumed in the building sector as the urbanization process accelerates. China's government attaches great importance to building energy efficiency (BEE). How does the urbanization process affect building energy consumption (BEC)? How much energy can be saved in the building sector through implementation of energy polices? These questions are critical for government development strategies and planning. Thus, in this paper, an econometric model (co-integration technique) is developed to analyze how China's BEC is affected by macroeconomic variables in the context of urbanization, and also forecast the BEC and BEE potential until 2020 under different scenarios. The research result shows that occupants’ lifestyles, residents’ living standards, and energy price have significant influences on BEC. The building sector is likely to contribute approximately 20% to China's energy conservation through electricity pricing reform. [ABSTRACT FROM AUTHOR]

In recent years, gas has begun to be widely used in power generation as well as in manufacturing because of the environmental advantages, lower cost, and the relative safety and reliability. Accurate prediction of future gas consumption is of great importance for energy security. In this paper, we first use path analysis to select the core factors that impact gas consumption, then use the RBFQRNN model and BVAR model separately to predict future gas consumption, and finally discuss and compare the results from the two models. The results show that if there is a positive growth in GDP, urbanization, and population, then gas consumption is expected to increase over a comparatively long time. In detail, gas consumption is expected to remain at about 25% of all energy consumption, GDP's contribution to gas consumption is expected to be about 53%, and the urbanization rate contribution would be about 15%. Gas consumption in China is predicted to reach around 178649.23 million cubic meters by the end of 2015 and about 264698.86 million cubic meters by 2020. [ABSTRACT FROM AUTHOR]

[Display omitted] The rapid development of urbanization has created high demand for central heating systems. This trend has led to an increase in energy consumption for central heating, which has made China's energy-saving transformation extremely difficult. However, the channel by which urbanization impacts and the future trends of energy consumption for central heating are still unclear. Therefore, this study employs the Logarithmic Mean Divisia Index (LMDI) method to decompose the effect of urbanization on changes in energy consumption for central heating during 2001–2019 in China. We investigate the contribution of urbanization from demographic, economic and spatial perspectives. Additionally, a scenario analysis is used to explore trends in energy consumption for central heating in different projected urbanization scenarios. The results show that urbanization has a positive impact on energy consumption for central heating, particularly in underdeveloped regions. Demographic urbanization has the strongest positive effect on energy consumption for central heating, followed by spatial urbanization and then economic urbanization. Compared with the expansion of area and economic development, population agglomeration will bring more energy consumption for central heating. The forecasting results indicate that energy consumption for central heating will reach 176–189 Mtce in 2030, which is approximately 1.4 times the current level. This work quantifies the energy consumption for central heating in the future under urbanization. The policy recommendations given at the end of the paper will provide a reference for the formulation of energy consumption for central heating. [ABSTRACT FROM AUTHOR]

Abstract: To find the key impact factors of CO2 emissions to realize the carbon intensity target, this paper examined the impact factors of population, economic level, technology level, urbanization level, industrialization level, service level, energy consumption structure and foreign trade degree on the energy-related CO2 emissions in Guangdong Province, China from 1980 to 2010 using an extended STIRPAT model. We employed ridge regression to fit the extended STIRPAT model. Empirical results indicate that factors such as population, urbanization level, GDP per capita, industrialization level and service level, can cause an increase in CO2 emissions. However, technology level, energy consumption structure and foreign trade degree can lead to a decrease in CO2 emissions. The estimated elastic coefficients suggest that population is the most important impact factor of CO2 emissions. Industrialization level, urbanization level, energy consumption structure, service level and GDP per capita are also significant impact factors, but the other factors such as technology level and foreign trade degree are less important impact factors. Some policy recommendations are also given on how to mitigate the growth of CO2 emissions. [Copyright &y& Elsevier]

Abstract: In China, Rapid economic growth has stimulated fast urban expansion and rural household income and consumption expenditure. In current paper, an input–output method is used to determine the impact of China’s increased urban and rural household consumption on carbon emissions. The results shows that the direct and indirect CO2 emission from household consumption accounted for more than 40% of total carbon emissions from primary energy utilization in China in 1992–2007. The population increase, expansion of urbanization and the increase of household consumption per capita all contribute to an increase of indirect carbon emissions, while carbon intensity decline mitigates the growth of carbon emissions. Therefore, at the domestic level, household consumption is of great significance for CO2 emission, which could be mitigated through changing the composition of goods and services consumed by households, and switching to consumption pattern of less carbon-intensive products. The government must consider the substantial contribution of household consumption to carbon emissions when China is encouraging consumption in order to address the current global financial crisis. [Copyright &y& Elsevier]

With the deepening of urbanization and industrialization, as well as the exacerbation of energy consumption, China is facing a severe situation in which nitrogen oxide (NOx) emissions reduction is imperative. In this study, it is aimed to put forward countermeasures and suggestions to reduce NOx emissions by analyzing the impact and mechanism of new urbanization, the agglomeration of energy-intensive industries and mutual interactions on China's NOx emissions. By analyzing the data of 30 provinces in China from 2006 to 2017, this paper adopted the system generalized method of moments (SYS-GMM) and intermediary effect model to introduce four variables, such as: energy efficiency, human capital, industrial structure and energy structure, which were for empirical analysis. From the results, it was shown that: (1) NOx emissions in China have an accumulated effect; (2) new urbanization inhibits NOx emissions, whilst the agglomeration of energy-intensive industries intensifies NOx emissions. New urbanization weakens the negative impact of the agglomeration of energy-intensive industries on NOx emissions reduction and, (3) among the impacts of new urbanization on NOx emissions, the energy efficiency and human capital reflect the intermediary effect mechanism. At the same time, in the impact of the agglomeration of energy-intensive industries on NOx emissions, the industrial structure and energy structure show the mechanisms of the intermediary effect and masking effect, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • Clean energy transitions based on the circular economy (CE) are analysed for a town in China. • This could reduce energy use, CO 2 emissions and PM 2.5 emissions compared to the BAU scenario. • COVID pandemic is a new opportunity to introduce circular economy measures. • Circular economy measures could accelerate clean energy transitions. This paper models the energy and emissions scenarios for a circular economy based clean energy transitions in a 140,000-population town in China, taking into account the new situation encountered by the COVID-19 pandemic. The modelled scenarios propose new clean energy transition roadmaps towards a sustainable urban system through the implementation of circular economy strategies. This is represented by the cascading use of industrial excess heat to form symbiosis between factories and to cover the growing building heat demand, as well as by the electrification of the transport sector and reusing the batteries for a second life as energy storage devices. The results show that for a circular economy scenario, during 2020–2040, an accumulated saving of 7.1 Mtoe final energy use (34%), a decline in 14.5 Mt CO 2 emissions (40%) and 592 t PM 2.5 emissions (43%) could be achieved compared with the business-as-usual scenario. The outcomes of the circular economy strategies are at least 7% better than the new policy scenario which simply has energy efficiency improvements. The outbreak of the COVID-19 tremendously impacts the socio-economic activities in the town. If taking the pandemic as an opportunity to enhance the circular economy, by 2040, compared with the scenario without introducing circular economy measures, the extra avoided final energy use, CO 2 emissions and PM 2.5 emissions could be 1.6 Mtoe (8%), 3.8 Mt (11%) and 229 t (17%) respectively. [ABSTRACT FROM AUTHOR]

On the basis of literature review and current situation analysis, models were established to carryout scientific measurement for carbon emissions and its intensity in China in this paper. With full consideration to the actual situation of energy consumption growth and carbon emission intensity controlling of energy consumption in new-type urbanization construction, the Kaya model was revised. With population intensity of GDP production, urbanization rate, employment rate of urban population, per capita energy consumption of urban employed population and carbon emission density of energy consumption selected as independent variables, test models of multivariate linear correlation were established for new-type urbanization construction, energy consumption growth and carbon emission intensity controlling of energy consumption, and the empirical equilibrium equation and error correction equation were determined through stability test, lag order test and co-integration test. The results are shown as follows: the urban employment rate (UER) has the greatest impact on carbon emission intensity, the per capita urban employment energy consumption (PEC) has the least impact on carbon emission intensity, and the degree of other factors in proper order is: urbanization rate (UR), population intensity of GDP production (PG) and carbon emission density (CD). Image 10106 • Energy consumption is the main source of China's carbon emissions and environmental pollution. • China's urbanization has contributed to the reduction of energy consumption and carbon emission intensity. • The change law of energy consumption carbon emission intensity in China's urbanization construction is analyzed. • Kaya model was adopted to study the influencing factors of carbon emission intensity control. [ABSTRACT FROM AUTHOR]

China's urbanization is rapidly proceeding while bringing marked impact on the energy consumption. This paper contributes to current literature by using an improved spatial econometric model Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) to investigate the effect of urbanization on energy consumption for China's regions. The impact of urbanization is further disaggregated into direct and indirect impacts from the perspective of spatial econometrics. Results show that, if the urbanization level increases by 1.0%, energy consumption levels will correspondingly decrease by 0.089%. The spatial spillover effect on adjacent areas is positive: a 1.0% increase in one specific area's urbanization level leads to a 0.136% increase in an adjacent area's energy consumption. It is suggested that, it is essential to devote major efforts to energy conservation, in the process of improving the new-type urbanization. In particular, disaggregating China's targets for controlling energy consumption into smaller targets for different regions will contribute to controlling the growth of energy consumption. [ABSTRACT FROM AUTHOR]