Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China’s County-Level Cities

Improving carbon efficiency and reducing carbon intensity are effective means of mitigating climate change. Carbon emissions due to urban residential energy consumption have increased significantly; however, there is a lack of research on urban residential carbon intensity. This paper examines the spatiotemporal variation of carbon intensity in the residential sector during 2001–2015, and then identifies the causes of the variation by utilizing the logarithmic mean Divisia index (LMDI) with the help of Microsoft Excel 2016 for 620 county-level cities in 30 Chinese provinces. The results show that high carbon intensity is mainly found in large cities, such as Beijing, Tianjin, and Shanghai. However, these cities showed a downward trend in carbon intensity. In terms of influencing factors, the energy consumption per capita, urban sprawl, and land demand are the three most influential factors in determining the changes in carbon intensity. The effect of energy consumption per capita mainly increases the carbon intensity, and its impact is higher in the municipal districts of provincial capital cities than in other types of cities. Similarly, the urban sprawl effect also promotes increases in carbon intensity, and a higher degree of influence appears in large cities. However, as urban expansion plateaus, the effect of urban sprawl decreases. The land-demand effect reduces the carbon intensity, and the degree of influence of the land-demand effect on carbon intensity is also clearly stronger in big cities. Our findings show that lowering the energy consumption per capita and optimizing the land-use structure are a reasonable direction of efforts, and the effects of differences in influencing factors should be paid more attention to reduce carbon intensity.

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The impact of energy consumption on Carbon Intensity of Human Well-Being (CIWB)

ECONOMICS AND BUSINESS ADMINISTRATION ◽

10.46223/hcmcoujs.econ.en.11.1.1360.2021 ◽

2021 ◽

Vol 11 (1) ◽

pp. 19-28

Author(s):

Nguyen Thuan ◽

Dang Bac Hai

Keyword(s):

Sustainable Development ◽

Energy Consumption ◽

Well Being ◽

Carbon Intensity ◽

Control Variables ◽

Human Welfare ◽

Middle Income ◽

Middle Income Countries ◽

Per Capita ◽

The Impact

A key concern when constructing sustainable development policy is reducing the negative impact on environmental systems and maximizing human welfare. In this study, we assess how energy consumption effected on Carbon intensity of human well-being (CIWB). Using two-way fixed effects in panel regression, this relationship has been investigated during 2000-2018 for 9 lower middle-income countries including Algeria, Bangladesh, Egypt, India, Morocco, Pakistan, Philippines, Uzbekistan and Vietnam, while adding GDP and FDI per capita as control variables. The study reveals that the use of energy for economic development is ineffective and inconsistent with the overview of sustainable development due to the result of increasing CIWB. However, the sign of negative coefficients of GDP and FDI per capita in control variables have given the striking findings that these factors will be helpful for lower middle - income countries to pursue sustainable development by reducing CIWB.

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The CO2 emissions drivers of post-communist economies in Eastern Europe and Central Asia

10.21203/rs.3.rs-21193/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Rui Li ◽

Jiang Hong ◽

Iryna Sotnyk ◽

Oleksandr Kubatko ◽

Ismail Almashaqbeh Y. A.

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Co2 Emissions ◽

Carbon Dioxide Emissions ◽

Slovak Republic ◽

Random Effect ◽

Random Effect Model ◽

High Energy ◽

Carbon Intensity ◽

Per Capita

Abstract Background. The CO2 emissions became a key environmental contaminant which is responsible for climate change in general and global warming in particular. Two geographical groups of countries that previously belonged to the former bloc of socialist countries are used for the estimations of CO2 emissions drivers of post-communist economies. The research covers such Eastern European countries as Bulgaria, Czech Republic, Hungary, Russian Federation, Poland, Romania, Slovak Republic, and Ukrainian territory as treated by international law and such Central Asian states as Kazakhstan and Uzbekistan during the period 1996-2018. The main goal of the research is to identify common drivers that determine carbon dioxide emissions in selected states. To control for the time fixed affects (like EU membership) random effect model was used for the analysis of panel data set.Results. It is found that energy efficiency has a negative influence on per capita CO2 emissions and an increase in GDP by 100 USD per 1 ton of oil decreases per capita CO2 emissions from 17 to 64 kg per capita. That is the more energy efficient the economy becomes, the less CO2 emissions per capita it produces. Unlike energy efficiency, an increase in GDP per capita by 1000 USD increases CO2 emissions by 260 kilograms per capita, and the richer the economy becomes, the more CO2 emissions per capita it generates. The increase in life expectancy by one year lead on average to increase in CO2 emissions per capita 200 to 370 kilograms per capita, with average values of 260 kilograms per capita. It was found that energy consumption per capita is a factor that positively adds to the CO2 emissions per capita. Oil prices, and foreign direct investment came as statistically insignificant factors.Conclusions. Among the main policy reconditions are the promotion of energy efficiency policy in accordance with EU policies and programs that stimulate a reduction in energy consumption and consequently CO2 emissions per capita. The other measure is the promotion of less energy-intensive service sector instead of building up an industrial sector characterized by high energy and carbon intensity.

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Temporal Evolution and Influencing Factors of Energy Consumption and Related Carbon Emissions from the Perspective of Industrialization and Urbanization in Shanghai, China

Sustainability ◽

10.3390/su10093054 ◽

2018 ◽

Vol 10 (9) ◽

pp. 3054 ◽

Cited By ~ 1

Author(s):

Pingxing Li ◽

Wei Sun

Keyword(s):

Energy Consumption ◽

Influencing Factors ◽

Carbon Emissions ◽

Temporal Evolution ◽

Added Value ◽

Living Standard ◽

Total Energy Consumption ◽

Consumption Level ◽

Economic Output ◽

Per Capita

Improvements of manufacturability and living standard driven by industrialization and urbanization typically cause a spike in total energy consumption (TEC) and related carbon emissions (TCEM). However, there have been few comparative studies to include industrial and residential energy consumption (IEC and REC, respectively) and related carbon emissions (ICEM and RCEM, respectively). Since China is a major emerging industrial country wherein urbanization is still ongoing, the present study was conducted in an attempt to analyze the temporal evolution of China’s continued energy consumption and related carbon emissions regarding both industrialization and urbanization. The influencing factors of TCEM, RCEM and ICEM are determined via the log-mean divisia index (LMDI) model. The results showed that both TEC and TCEM gradually increased (apart from a slight decrease in 2014); REC and RCEM increased steadily with no sharp peak; while IEC and ICEM declined sharply. TCEM was positively affected by economic output, consumption level, and population size; the influence of consumption level became more and more significant. Per capita GDP and per capita expenditure were the most significant driving factors for RCEM, while industrial added value (IAV) was the main driving factor for ICEM. The temporal evolution and influencing factors of energy consumption and carbon emissions had stage-related characteristics in accordance with Shanghai’s three stages of development. The Shanghai case study provided a comprehensive understanding of energy consumption and related carbon emissions from the dual perspective of industrialization and urbanization.

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Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model

Energies ◽

10.3390/en12163054 ◽

2019 ◽

Vol 12 (16) ◽

pp. 3054 ◽

Cited By ~ 1

Author(s):

Zhen Li ◽

Yanbin Li ◽

Shuangshuang Shao

Keyword(s):

Energy Consumption ◽

Influencing Factors ◽

Carbon Emissions ◽

Total Population ◽

Industrial Structure ◽

Global Climate ◽

Primary Energy ◽

Urbanization Level ◽

Per Capita ◽

Technology Level

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020.

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Analysis on the Energy Consumption (EC) and Carbon Emission (CE) of Tourism Transport of Jiangxi Province Using the PLS Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.535.533 ◽

2014 ◽

Vol 535 ◽

pp. 533-536

Author(s):

Jun Song Jia ◽

Cai Hua Kuang ◽

Lin Lin Hu

Keyword(s):

Energy Consumption ◽

Carbon Emission ◽

Local Development ◽

Tourism Industry ◽

Gdp Per Capita ◽

Carbon Intensity ◽

Annual Growth Rate ◽

Per Capita ◽

The Impact ◽

Pls Method

Taking Jiangxi of China as an example, we, firstly, accounted the energy consumption (EC) and carbon emission (CO2, CE) of this provinces tourism transport in recently 13 years. Then, we used the Partial Least Squares (PLS) method to analysis the drivers of the CE. Results show that: 1Respectively, the EC and CE of tourism transport in Jiangxi in 1999 were 4.2 PJ and 0.46 Mt. They grew up to 31.9 PJ and 3.59 Mt in 2011. The increasing amounts were 27.7 PJ and 3.13 Mt, with an average annual growth rate of 18.4% and 18.6%. These meant that with the improvement of living standards, more and more people engaged in the activities of tourism industry. 2The consumption demand of peoples tourism had been greatly released in 2004 and 2011, which could arise from the influences of the "SARS" in 2003 and the global financial crisis in 2008, respectively. 3The importance of the latent drivers can be sorted as the following order: A2 (square of GDP per capita), A (GDP per capita), T (carbon intensity), T1 (EC intensity) and P (population). The impact on the CE from T2 (the factor denoted by T/T1) is negligible. The impact of U (urbanization rate) is little. The A2, A, T, T1 and P have an increase of 1%. The corresponding CE will have an increase of 0.275%, 0.259%, 0.148%, 0.145% and 0.131%, respectively. In the end, some suggestions are proposed for local development: to speed up the pattern's upgrade of the development, to promote the implementation of energy saving, to improve the technical level and energy efficiency so as to reduce the regional energy intensity, to go on controlling population growth and to boost the new-type urbanization, to some extent.

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On the Impact of GDP per Capita, Carbon Intensity and Innovation for Renewable Energy Consumption: Worldwide Evidence

Energies ◽

10.3390/en14196254 ◽

2021 ◽

Vol 14 (19) ◽

pp. 6254

Author(s):

Cristiana Tudor ◽

Robert Sova

Keyword(s):

Renewable Energy ◽

Energy Consumption ◽

Research And Development ◽

High Income ◽

Gdp Per Capita ◽

Carbon Intensity ◽

Renewable Energy Consumption ◽

Per Capita ◽

World Level ◽

Very High

The mitigation of climate change through ambitious greenhouse gases emission reduction targets constitutes a current priority at world level, reflected in international, regional and national agendas. Within the common framework for global climate action, an increased reliance on renewable energy sources, which would assist countries to reduce energy imports and cut fossil fuel use, emerged as the solution towards achieving worldwide energy security and sustainability through carbon-neutrality. As such, this study is aimed to investigate the heterogeneous effects of relevant economic and environmental driving factors for renewable energy consumption (REC) that emerge from current policy objectives (GDP per capita, carbon intensity, and research and development) through an empirical analysis of a wide panel of 94 countries, and five income-based subpanels, over the 1995–2019 period, by using heterogeneous panel data fixed-effects estimation techniques (static and dynamic) with robust Driscoll–Kraay standard errors. The results unambiguously indicate that CO2 intensity has a significant mitigating effect on REC at world level, and this relationship is stronger for low-income and very high-income countries. Moreover, GDP per capita promotes REC when it surpasses the 5000 USD threshold, whereas research and development is a major contributor to increase in renewable energy consumption in very high-income countries. As such, for the policy makers, it is necessary to consider the heterogeneity of the drivers of REC in order to issue effective and congruent policies. The effective employment of post-COVID-19 recovery funds constitutes a timely, ideal occasion.

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Analysis of Influencing Factors of Carbon Emissions in China’s Logistics Industry: A GDIM-Based Indicator Decomposition

Energies ◽

10.3390/en14185742 ◽

2021 ◽

Vol 14 (18) ◽

pp. 5742

Author(s):

Changyou Zhang ◽

Wenyu Zhang ◽

Weina Luo ◽

Xue Gao ◽

Bingchen Zhang

Keyword(s):

Influencing Factors ◽

Carbon Emissions ◽

Carbon Emission ◽

Added Value ◽

Total Carbon ◽

Carbon Intensity ◽

Contribution Ratio ◽

Logistics Industry ◽

Per Capita ◽

First Time

Due to increased global carbon dioxide emissions, the greenhouse effect is being aggravated, which has attracted wide attention. China is committed to promoting the low-carbon development of all industries. This paper analyzed the influencing factors of carbon emissions in the Chinese logistics industry, so as to identify the key factors that influence carbon emissions. Based on the carbon emission data of China’s logistics industry in 2000–2019, this paper applied the carbon emission coefficients issued by the Intergovernmental Panel on Climate Change. For the first time, the Generalized Divisia Index Method was used to analyze the degree of influence of the factors on carbon emissions. This method considered more variables and their relationships. The results showed that (1) the carbon emissions of the logistics industry were increased by 3.22 times from 2000 to 2018, and showed negative growth for the first time in 2019; (2) the added value of the logistics industry is the most important factor in increasing carbon emissions (with a contribution ratio of 65.45%), energy consumption and practical population size are the main factors in carbon emissions. The promotion of this industry is subjected to decreased per capita carbon emissions, which have a large impact on total carbon emissions; (3) the intensity of carbon output is the most important factor in the reduction of carbon emissions (with a contribution ratio of −29.1%), where the energy carbon intensity and per capita added value are the main influencing factors with regard to the reduction of carbon emissions, while energy intensity has a negative inhibitory effect on carbon emissions, and (4) the influencing factors have negative effects on the cumulative inhibition of carbon emissions in the logistics industry, to an extent that is far less than the integral promotion of carbon emissions. Finally, according to the research conclusions of this paper, it is feasible to make recommendations for the carbon reduction of the logistics industry.

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Time Series Analysis of Energy Consumption and Carbon Emission for Binhai New Area of Tianjin

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.174-177.3571 ◽

2012 ◽

Vol 174-177 ◽

pp. 3571-3575 ◽

Cited By ~ 1

Author(s):

Chun Li Chu ◽

Yi Fang Yang ◽

Xue Bai ◽

Qian Peng ◽

Mei Ting Ju

Keyword(s):

Time Series ◽

Energy Consumption ◽

Emission Intensity ◽

Co2 Emission ◽

Carbon Emission ◽

Energy Intensity ◽

Carbon Intensity ◽

Per Capita ◽

Tianjin City ◽

Co2 Emission Intensity

With the rapid development of industrialization and urbanization, cities become the centers to address the problem of climate change for China. Binhai New Area of Tianjin city plays an important role to boost the economy of North China according to the long-term development planning of China. It is essential for Binhai New Area of Tianjin to promote energy efficiency and reduce the CO2 emission intensity. The study explores the characteristics of the energy consumption, energy intensity, carbon emission and carbon intensity of Binhai New Area through time series analysis. We conclude that the consumption of energy has increased with an annual growth rate of 17.9% from 2000 to 2009. The energy consumption per capita increases from 4.32 tons of SCE per capita in 2000 to 12.37 tons of SCE per capita in 2009, which is much higher than that of Tianjin city and also China as a whole. The energy intensity has declined from 0.79 tons of SCE/104Y in 2000 to 0.38 tons of SCE/104Y in 2009. But it is lower than that of Tianjin. Total carbon emission has increased by 225% from 2000 to 2009. The carbon emission per capita increases from 10.8 tons per capita in 2000 to 30.8 tons per capita in 2009. The carbon intensity has declined from 1.97 tons /104Y in2000 to 0.96 tons/104Y in 2009. Thus, we suggest that the composition of energy consumption should be optimized and more clean energy should be used to reduce the total CO2 emission and CO2 emission intensity.

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Toward a Dualistic Growth? Population Increase and Land-Use Change in Rome, Italy

Land ◽

10.3390/land10070749 ◽

2021 ◽

Vol 10 (7) ◽

pp. 749

Author(s):

Leonardo Bianchini ◽

Gianluca Egidi ◽

Ahmed Alhuseen ◽

Adele Sateriano ◽

Sirio Cividino ◽

...

Keyword(s):

Land Use ◽

Land Use Change ◽

Population Growth ◽

Mediterranean Basin ◽

Urban Expansion ◽

Arable Land ◽

Central Italy ◽

Agricultural Landscapes ◽

Population Increase ◽

Per Capita

The spatial mismatch between population growth and settlement expansion is at the base of current models of urban growth. Empirical evidence is increasingly required to inform planning measures promoting urban containment in the context of a stable (or declining) population. In these regards, per-capita indicators of land-use change can be adopted with the aim at evaluating long-term sustainability of urbanization processes. The present study assesses spatial variations in per-capita indicators of land-use change in Rome, Central Italy, at five years (1949, 1974, 1999, 2008, and 2016) with the final objective of quantifying the mismatch between urban expansion and population growth. Originally specialized in agricultural productions, Rome’s metropolitan area is a paradigmatic example of dispersed urban expansion in the Mediterranean basin. By considering multiple land-use dynamics, per-capita indicators of landscape change delineated three distinctive waves of growth corresponding with urbanization, suburbanization, and a more mixed stage with counter-urbanization and re-urbanization impulses. By reflecting different socioeconomic contexts on a local scale, urban fabric and forests were identified as the ‘winner’ classes, expanding homogeneously over time at the expense of cropland. Agricultural landscapes experienced a more heterogeneous trend with arable land and pastures declining systematically and more fragmented land classes (e.g., vineyards and olive groves) displaying stable (or slightly increasing) trends. The continuous reduction of per-capita surface area of cropland that’s supports a reduced production base, which is now insufficient to satisfy the rising demand for fresh food at the metropolitan scale, indicates the unsustainability of the current development in Rome and more generally in the whole Mediterranean basin, a region specialized traditionally in (proximity) agricultural productions.

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Urban Sustainability at the Cost of Rural Unsustainability

Sustainability ◽

10.3390/su13105466 ◽

2021 ◽

Vol 13 (10) ◽

pp. 5466

Author(s):

Guangwei Huang

Keyword(s):

Urban Sprawl ◽

Rural Areas ◽

Urban Sustainability ◽

Negative Externalities ◽

Large Cities ◽

Rural Sustainability ◽

Use Of Resources ◽

The Cost ◽

New Framework ◽

Japan And China

Urban sustainability refers to building and maintaining cities that can continue to function without running out of resources. However, growing cities require more land and urban sprawl has transformed surrounding rural areas into urbanized settlements. Furthermore, the prosperity of large cities depends on the supply of both natural and human resources from rural areas, either nearby or remote. On the other hand, the use of resources of rural areas by cities may cause negative externalities to rural areas, affecting their sustainability. Therefore, a critical, but very much neglected issue, is how unban sustainability should be pursued without affecting rural sustainability. In this study, cases in Japan and China were analyzed from resources and population migration perspectives to provide evidence for the possibility that urban sustainability might have been pursued at the cost of rural unsustainability. It was intended to develop a better understanding of urban sustainability through the lens of externalities. Based on the analysis, a new framework for urban sustainability study was proposed, which consists of three new pillars. Namely, externality, vulnerability, and population instability.

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