Examining the impact factors of urban residential energy consumption and CO2 emissions in China – Evidence from city-level data

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 CO₂ 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 CO₂ 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 CO₂ 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 CO₂ 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 CO₂ emissions.     

Do nuclear and renewable energy improve the environment? Empirical evidence from the United States

The central focus of this article is to assess the dynamic effects of nuclear and renewable energy consumption on CO₂ emissions, for a given level of income and energy consumption. We apply an autoregressive distributed lag (ARDL) approach to cointegration to U.S. data from 1960 to 2010. We find that nuclear energy consumption indeed reduces CO₂ emissions in both the short- and long-run, while renewable energy consumption does only in the short-run. We also find that income increases CO₂ emissions in the long-run after showing the environmental Kuznets curve (EKC) initially in the short-run. Finally, energy consumption is found to have a negative impact on reducing CO₂ emissions in the short- and long-run.     

Parallel application-level behavioral attributes for performance and energy management of high-performance computing systems

Run time variability of parallel applications continues to present significant challenges to their performance and energy efficiency in high-performance computing (HPC) systems. When run times are extended and unpredictable, application developers perceive this as a degradation of system (or subsystem) performance. Extended run times directly contribute to proportionally higher energy consumption, potentially negating efforts by applications, or the HPC system, to optimize energy consumption using low-level control techniques, such as dynamic voltage and frequency scaling (DVFS). Therefore, successful systemic management of application run time performance can result in less wasted energy, or even energy savings. We have been studying run time variability in terms of communication time, from the perspective of the application, focusing on the interconnection network. More recently, our focus has shifted to developing a more complete understanding of the effects of HPC subsystem interactions on parallel applications. In this context, the set of executing applications on the HPC system is treated as a subsystem, along with more traditional subsystems like the communication subsystem, storage subsystem, etc. To gain insight into the run time variability problem, our earlier work developed a framework to emulate parallel applications (PACE) that stresses the communication subsystem. Evaluation of run time sensitivity to network performance of real applications is performed with a tool called PARSE, which uses PACE. In this paper, we propose a model defining application-level behavioral attributes, that collectively describes how applications behave in terms of their run time performance, as functions of their process distribution on the system (spacial locality), and subsystem interactions (communication subsystem degradation). These subsystem interactions are produced when multiple applications execute concurrently on the same HPC system. We also revisit our evaluation framework and tools to demonstrate the flexibility of our application characterization techniques, and the ease with which attributes can be quantified. The validity of the model is demonstrated using our tools with several parallel benchmarks and application fragments. Results suggest that it is possible to articulate application-level behavioral attributes as a tuple of numeric values that describe course-grained performance behavior.     

CO2, economic growth,and energy consumption in China's provinces: Investigating the spatiotemporal and econometric characteristics of China's CO2 emissions

This study addresses the spatiotemporal variations at play in China's CO₂ emissions, based on an estimation of emission levels in the period 1995–2012 and an provincial analysis of the relationship of CO₂ emissions to economic growth and energy consumption. Using a series of econometric models and data on the combustion of fossil fuels and cement manufacturing, the study first estimated CO₂ emission levels during the study period, exploring their spatiotemporal pattern. The results indicate that both China's total and its per capita CO₂ emissions have increased significantly over the study period, with both measures evidencing a similar evolution (albeit one that is characterized by noticeable regional discrepancies at the provincial level and which displays properties of convergence). From a geographical perspective, we found both total and per capita CO₂ emissionsto be higher in China's eastern region than in the country's central and western regions. Panel data analysis was subsequently undertaken in order to quantify the dynamic casual relationship between economic growth, energy consumption, and CO₂ emissions. The empirical results indicated that the variables were in fact cointegrated and exhibited a long-run positive relationship. The results of further Granger causality tests indicated the existence of a bidirectional positive causality between economic growth and energy consumption, as well as between energy consumption and CO₂ emissions, and a unidirectional positive causality running from economic growth to CO₂ emissions. The findings of this study suggest that China is, in the long run, dependent on carbon energy consumption for its rapid economic growth, a dependency which is the cause of considerable increases in CO₂ emissions. China should therefore make greater efforts to develop low-carbon technologies and renewable energy, and improve energy efficiency in order to reduce emissions and achieve green economic growth.     

能源活动CO2排放不同核算方法比较和减排策略选择

能源活动CO₂排放是温室气体排放的最重要部分,这部分CO₂排放量的核算是温室气体清单编制和减排方案制定的关键和基础。采用直接法、电热终端法和隐含终端法核算了2009年中国能源消费的CO₂排放量,对不同核算法的CO₂排放部门分布、部门排放强度进行了比较,明确不同核算方法的差异和适用范围。采用电热终端法的核算结果定量分析了各产业部门和工业行业的经济增长和排放强度变化对中国能源活动CO₂排放增长的影响。结果表明,中国2009年隐含终端CO₂排放量为65.6亿t,略高于直接和电热终端CO₂排放量62.2亿t。3种核算方法的CO₂排放部门分布和排放强度有明显的差异:电、热力生产与供应业的直接排放占比为45.2%,而电热终端CO₂排放仅占4.5%;制造业的直接法、电热终端法和隐含终端法核算的CO₂排放占比分别为35.3% 、61.1%和65.5%,是终端能源消费CO₂排放最主要的部门;制造业、电热力生产与供应业和交通运输业的电热终端CO₂排放强度分别为2.166、1.72和1.622 t CO₂/万元GDP,是排放强度较高的部门。在产业部门中,制造业的色金属冶炼及压延加工业、非金属矿物制品业等5个行业以9.8%的经济增长贡献,排放了52.4%的CO₂,是产业结构调整、技术和工程减排的重点;服务业以7.2%的CO₂排放,贡献了38.4%的经济增长,应作为中国低碳经济优先发展的产业。     

Testing environmental Kuznets curve hypothesis: The role of renewable and non-renewable energy consumption and trade in OECD countries

This paper investigates the causal relationships between per capita CO₂ emissions, gross domestic product (GDP), renewable and non-renewable energy consumption, and international trade for a panel of 25 OECD countries over the period 1980–2010. Short-run Granger causality tests show the existence of bidirectional causality between: renewable energy consumption and imports, renewable and non-renewable energy consumption, non-renewable energy and trade (exports or imports); and unidirectional causality running from: exports to renewable energy, trade to CO₂ emissions, output to renewable energy. There are also long-run bidirectional causalities between all our considered variables. Our long-run fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) estimates show that the inverted U-shaped environmental Kuznets curve (EKC) hypothesis is verified for this sample of OECD countries. They also show that increasing non-renewable energy increases CO₂ emissions. Interestingly, increasing trade or renewable energy reduces CO₂ emissions. According to these results, more trade and more use of renewable energy are efficient strategies to combat global warming in these countries.     

Life cycle inventory analysis of co<Subscript>2</Subscript> emissions manufacturing commodity plastics in japan

The goal of this study was to calculate the average CO₂ emissions for manufacturing three commodity plastics, polyethylene (PE), polypropylene (PP), and polyvinyl chloride (PVC) in Japan. The CO₂ emissions were calculated from cradle to gate, excluding the calcination processes after use. As the results, the followings were observed: 1) The gross CO₂ emissions for the manufacture of plastics in Japan were 1.3, 1.4, and 1.7 kg-CO₂/kg-PE, PP, and PVC, respectively. These mainly reflected the difference of CO₂ emissions for the in-house electricity generation. 2) The CO₂ emissions for the electricity used for manufacturing PVC were higher than that used for PE and PP, because additional electricity was required for the electrolysis to produce chlorine. The gross electricity consumption for manufacturing PVC was 1.3 kWh/kg-PVC, and the other plastics consumed 0.5 kWh/kg-Products. In addition, the effects of energy saving were studied using a projected gas-diffusion electrode for the electrolysis of salt on the reduction of CO₂ emissions. It was estimated that the reduction in CO₂ emissions was 7% compared with the present PVC manufacturing processes.     

Can energy policies affect the cycle of carbon emissions? Case study on the energy consumption of industrial terminals in Shanghai,Jiangsu and Zhejiang

This paper proposes an approach to calculate the time series of cumulative carbon dioxide (CO₂) emissions between 1995 and 2014 based on industrial energy consumption data in three Eastern China jurisdictions in Shanghai, Jiangsu and Zhejiang during these two decades. Using the Hodrick-Prescott filter, the fluctuation components of the cumulative CO₂-emission time-series data in the three provinces are obtained. Subsequently, a grey correlation-based change-point search algorithm is used to determine change-points in these data. Additionally, the CO₂-emission time-series is divided into stages based on the change-points. The cycle characteristics of national energy policies, laws, and regulations are compared with those of the cumulative CO₂-emission cycle of the three provinces to analyse the impact of energy policies on CO₂ emissions. This study shows that, although the industrial structure and trends in the CO₂ emission time-series data of the three provinces are different, their cumulative CO₂-emission cycle remains the same from 1995 to 2014. The variation characteristics of the cumulative CO₂ emissions for each cycle during this period are well aligned with the stage characteristics of energy policies, laws, and regulations, indicating that energy policies play a consistent role in regulating such emissions. This study examines low-carbon production and sustainable energy development, and offers suggestions for issuing and perfecting energy policies, laws, and regulations, considering the indicators of energy consumption and CO₂ emissions.     

Decoupling environmental pressure from economic growth on city level: The Case Study of Chongqing in China

As cities represent the microcosms of global environmental change, it is very important for the global sustainable development by decoupling environmental pressure from economic growth on city level. In this paper, the municipality of Chongqing in China is employed as a case to show whether the decoupling of environmental pressures from economic growth has occurred in cities undergoing rapid economic growth; what is the level of decoupling; and what causes the observed degree of decoupling. Results show the following. (1) During the period of 1999–2010, decoupling from economic growth has been absolute for the emissions of SO₂, soot, and waste water, while it has been relative for total energy consumption, emissions of CO₂ and solid waste. (2) Compared with the period 2000–2005, decoupling level improved for all the six environmental pressures in the period 2005–2010. (3) Compared with China and other three municipalities of China, the overall decoupling level of Chongqing is above China’s average while below those of Beijing and Shanghai. (4) During the period 1999–2000, technological change was the dominate factor for decoupling Chongqing’s environmental pressure from economic growth, as it contributed 131.4%, 134.6%, 99.9%, 97.7%, 104.5% and 54.9% to the decoupling of total energy consumption, emissions of CO₂, SO₂, soot, waste water and solid waste, respectively; while economic structural change had very tiny effect to the decoupling of emissions of soot and SO₂, and it even had negative effect to that of total energy consumption, and emissions of CO₂ and waste water. Based on the above observations, we explain the difference in decoupling levels for different environmental pressures and suggest approaches for policy-makers on further promoting decoupling environmental pressure from economic growth.     

我国典型城市化石能源消费CO2排放及其影响因素比较研究

城市是化石能源消费和CO₂排放的主要区域。分析典型城市化石能源消费CO₂排放特征,明确不同城市CO₂排放动态及主要影响因素的差异,是开展城市减排行动的重要科学依据。采用IPCC推荐方法及中国的排放参数核算11个典型城市2006—2015年间化石能源消费产生的CO₂排放量。根据各城市经济发展和CO₂排放特征将之分为四类:经济高度发达城市(北京、上海、广州)、高碳排放城市(重庆、乌鲁木齐、唐山)、低排放低增长城市(哈尔滨、呼和浩特和大庆)和低排放高增长城市(贵阳、合肥),并运用对数平均迪氏指数法(Logarithmic Mean Divisia Index,即LMDI分解法)对比分析了四类城市CO₂排放量的影响因素。结果表明:(1)研究期内大部分城市CO₂排放总量有所增加,仅北京和广州呈下降趋势,工业部门CO₂排放在城市排放总量及其变化中占据主导地位;四类城市的人均CO₂排放量表现出与排放总量相...     

辽宁省能源消费和碳排放与经济增长的关系

在广泛收集资料的基础上,对辽宁省的能源利用效率、能源消费强度与经济增长的关系进行探索,其目的为辽宁省的节能与CO2减排及经济的快速发展提供科学依据。结果表明:辽宁整体单位GDP能耗高出全国水平52%—70%,第二产业单位GDP能耗是第三产业的5.67—8.41倍,第一产业的7.2—9.0倍;辽宁能源利用率只有全国平均水平的60%左右,第二产业能源利用效率只有第一产业的11.89%,第三产业的12.60%;GDP年增长速率大于能源消费量年增长速率,能源投入增加促进了国民生产总值的提高,但是经济增长并不是完全依赖能源消费的增长;能源消费量与经济增长的关系,呈现出"N型"曲线特征,随着GDP的增加,能源消费量出现反复上升和下降过程,辽宁省能源消费和经济增长关系没有达到长期的均衡性,尚处于非平衡的发展阶段。     

Economic growth,electricity consumption,urbanization and environmental degradation relationship in United Arab Emirates

The present study explores the relationship between economic growth, electricity consumption, urbanization and environmental degradation in case of United Arab Emirates (UAE). The study covers the quarter frequency data over the period of 1975–2011. We have applied the ARDL bounds testing approach to examine the long run relationship between the variables in the presence of structural breaks. The VECM Granger causality is applied to investigate the direction of causal relationship between the variables. Our empirical exercise reported the existence of cointegration among the series. Further, we found an inverted U-shaped relationship between economic growth and CO₂ emissions i.e. economic growth raises energy emissions initially and declines it after a threshold point of income per capita (EKC exists). Electricity consumption declines CO₂ emissions. The relationship between urbanization and CO₂ emissions is positive. Exports seem to improve the environmental quality by lowering CO₂ emissions. The causality analysis validates the feedback effect between CO₂ emissions and electricity consumption. Economic growth and urbanization Granger cause CO₂ emissions.     

城市三维空间结构对碳排放影响的尺度效应

城市是碳排放最集中的区域,全面厘清城市空间结构对碳排放的影响对碳减排规划具有重要意义。以往研究主要关注城市二维结构与碳排放的关系,表明城市扩张是碳排放剧增的主要原因。虽然城市三维空间结构也会显著影响碳排放,然而其影响的尺度效应依然缺少深入分析。为此以广州市为例,结合相关性分析、随机森林探究三维空间结构与碳排放的关系,并揭示三维空间结构影响的尺度效应。研究结果表明：（1）（高层）建筑物密度、建筑覆盖率、容积率与人口密度是碳排放的关键影响因素,主要通过直接增加人类活动或加剧热岛效应使得能源消耗和碳排放增多;（2）三维空间结构对碳排放的影响具有明显的尺度效应。随着分析尺度的变化,碳排放受三维空间结构的不同方面主导;（3）广州作为紧凑型城市的代表,如果片面追求城市三维空间的紧凑布局将不利于低碳城市的发展。因此,相关部门应重视宏观尺度下的三维空间结构的合理布局,合理开发城市边缘地区,降低城市中心建筑物的紧凑布局,构建多中心的城市格局,以有效降低碳排放水平,促进低碳城市的构建与可持续发展。研究所得成果可为城市建筑三维空间布局的合理优化提供参考依据,助力"双碳"目标的实现。     

A Comparative Study of Environmental Impacts of Two Delivery Systems in the Business‐to‐Customer Book Retail Sector

China has the highest carbon dioxide (CO₂) emissions in the world. In China, logistics accounts for a significant portion of the total energy use and CO₂ emissions in business‐to‐customer (B2C) retailing. This study focuses on the environmental impacts of B2C delivery in China, focusing on the book retail industry. Mathematical models are proposed based on the practical operations of the “e‐commerce networked delivery” (END) system and the “sustainable networked delivery” (SND) system. The energy consumption and CO₂ emissions per book are then determined and compared for the two systems. Furthermore, we contrast the findings with those of similar studies conducted for other countries and provide explanations for the differences. The results show that (1) in general, in China, the SND system is better than the END system in terms of environmental impacts; (2) the END system in China generates fewer environmental impacts than those in the United States and the United Kingdom, while the SND system in China has greater environmental impacts than that in the United States; and (3) the wide use of vehicles such as electric bicycles that have low energy consumption rates contributes to the reduction of environmental impacts per book in both the END and SND systems in China. The limitations of the study and suggestions for future research are also discussed.     

Estimating the environmental Kuznets curve for Spain by considering fuel oil prices (1874–2011)

We perform a structural analysis on an environmental Kuznets curve (EKC) for Spain by exploiting long time series (1874–2011) and by using real oil prices as an indicator of variations in fuel energy consumption. This empirical strategy allows us to both, capture the effect of the most pollutant energy on carbon dioxide (CO₂) emissions and, at the same time, preclude potential endogeneity problems derived from the direct inclusion of fuel consumption in econometric specification. Knowing the extent to which oil prices affect CO₂ emissions has a straightforward application for environmental policy. The dynamics estimates of the long and short-term relationships among CO₂, economic growth and oil prices are built through an autoregressive distributed lag (ARDL) model. Our test results support the EKC hypothesis. Moreover, real oil prices are clearly revealed as a valuable indicator of pollutant energy consumption.     

SOCCER: Self-Optimization of Energy-efficient Cloud Resources

Cloud data centers often schedule heterogeneous workloads without considering energy consumption and carbon emission aspects. Tremendous amount of energy consumption leads to high operational costs and reduces return on investment and contributes towards carbon footprints to the environment. Therefore, there is need of energy-aware cloud based system which schedules computing resources automatically by considering energy consumption as an important parameter. In this paper, energy efficient autonomic cloud system [Self-Optimization of Cloud Computing Energy-efficient Resources (SOCCER)] is proposed for energy efficient scheduling of cloud resources in data centers. The proposed work considers energy as a Quality of Service (QoS) parameter and automatically optimizes the efficiency of cloud resources by reducing energy consumption. The performance of the proposed system has been evaluated in real cloud environment and the experimental results show that the proposed system performs better in terms of energy consumption of cloud resources and utilizes these resources optimally.     

Relationships among carbon emissions,economic growth,energy consumption and population growth: Testing Environmental Kuznets Curve hypothesis for Brazil,China, India and Indonesia

This study examines the impacts of income, energy consumption and population growth on CO₂ emissions by employing an annual time series data for the period 1970–2012 for India, Indonesia, China, and Brazil. The study used the Autoregressive Distributed Lag (ARDL) bounds test approach considering both the linear and non-linear assumptions for related time series data for the top CO₂ emitter emerging countries in both the short run and long run. The results show that CO₂ emissions have increased statistically significantly with increases in income and energy consumption in all four countries. While the relationship between CO₂ emissions and population growth was found to be statistically significant for India and Brazil, it has been statistically insignificant for China and Indonesia in both the short run and long run. Also, empirical observations from the testing of environmental Kuznets curve (EKC) hypothesis imply that in the cases of Brazil, China and Indonesia, CO₂ emissions will decrease over the time when income increases. So based on the EKC findings, it can be argued that these three countries should not take any actions or policies, which might have conservative impacts on income, in order to reduce their CO₂ emissions. But in the case of India, where CO₂ emissions and income were found to have a positive relationship, an increase in income over the time will not reduce CO₂ emissions in the country.     

Life cycle inventory analysis on Bio-DME and/or Bio-MeOH products through BLUE tower process

Background, aims, and scope  

In this study, we focused on the biomass di-methyl ether (Bio-DME) and the biomass methanol (Bio-MeOH) in BTL (Biomass to Liquid) fuels which might bring a solution on an energy storage and/or CO₂ emissions abatement. For these fuels, our object is to estimate CO₂ emissions and energy intensities (the specific energy consumption in each sub-process) for the biomass liquefaction system, which is expanded to material’s transportation, energy conversion and fuel transportation in detail.     

A thermal-aware energy-efficient virtual machine placement algorithm based on fuzzy controlled binary gravitational search algorithm (FC-BGSA)

The remarkable growth of cloud computing applications has caused many data centers to encounter unprecedented power consumption and heat generation. Cloud providers share their computational infrastructure through virtualization technology. The scheduler component decides which physical machine hosts the requested virtual machine. This process is virtual machine placement (VMP) which, affects the power distribution, and thereby the energy consumption of the data centers. Due to the heterogeneity and multidimensionality of resources, this task is not trivial, and many studies have tried to address this problem using different methods. However, the majority of such studies fail to consider the cooling energy, which accounts for almost 30% of the energy consumption in a data center. In this paper, we propose a metaheuristic approach based on the binary version of gravitational search algorithm to simultaneously minimize the computational and cooling energy in the VMP problem. In addition, we suggest a self-adaptive mechanism based on fuzzy logic to control the behavior of the algorithms in terms of exploitation and exploration. The simulation results illustrate that the proposed algorithm reduced energy consumption by 26% in the PlanetLab Dataset and 30% in the Google cluster dataset relative to the average of compared algorithms. The results also indicate that the proposed algorithm provides a much more thermally reliable operation.

     

Toward a Carbon Dioxide Neutral Industrial Park

The industrial park of Herdersbrug (Brugge, Flanders, Belgium) comprises 92 small and medium‐sized enterprises, a waste‐to‐energy incinerator, and a power plant (not included in the study) on its site. To study the carbon dioxide (CO₂) neutrality of the park, we made a park‐wide inventory for 2007 of the CO₂ emissions due to energy consumption (electricity and fossil fuel) and waste incineration, as well as an inventory of the existing renewable electricity and heat generation. The definition of CO₂ neutrality in Flanders only considers CO₂ released as a consequence of consumption or generation of electricity, not the CO₂ emitted when fossil fuel is consumed for heat generation. To further decrease or avoid CO₂ emissions, we project and evaluate measures to increase renewable energy generation. The 21 kilotons (kt) of CO₂ emitted due to electricity consumption are more than compensated by the 25 kt of CO₂ avoided by generation of renewable electricity. Herdersbrug Industrial Park is thus CO₂ neutral, according to the definition of the Flemish government. Only a small fraction (6.6%) of the CO₂ emitted as a consequence of fossil fuel consumption (heat generation) and waste incineration is compensated by existing and projected measures for renewable heat generation. Of the total CO₂ emission (149 kt) due to energy consumption (electricity + heat generation) and waste incineration on the Herdersbrug Industrial Park in 2007, 70.5% is compensated by existing and projected renewable energy generated in the park. Forty‐seven percent of the yearly avoided CO₂ corresponds to renewable energy generated from waste incineration and biomass fermentation.