Special Issue "Low Carbon Transitions Worldwide"
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A special issue of Energies (ISSN 1996-1073).
Deadline for manuscript submissions: closed (15 November 2011)
Special Issue Editor
Guest Editor
Dr. Bin Chen
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, 19 Xinjiekouwai Street, Haidian District, Beijing, China
E-Mail: chenb@bnu.edu.cn
Phone: (86) 10-5880 7368
Special Issue Information
Dear Colleagues,
The rising concern on climate change as indicated by the global focus on the Copenhagen Summit implies a possible, or almost inevitable, shift of current economic paradigm to a low-carbon one. Making the transition to a low carbon economy requires active participation of, and presents a significant challenge to the global society, which is currently locked into high carbon technological economic and social regimes.
Actions have been taken in many developed or developing countries by either establishing planning framework or enacting progressive policies to reduce carbon emissions in each of the production sectors and obtain a ‘cleaner’ production paradigm. In the main, these actions have led to effectual reductions in energy consumption and pollution emissions in the focus sectors. For example, the investment in renewable energy suppliers, closure of inefficient power plants and upgrading emissions standards have led to significant decreases in air and water pollution. It is clear that some leading countries have successfully taken the first few steps towards sustainable development. However, as with the development of low carbon-targeted policies, it will be useful to develop scientific and reliable models and decision support systems to check which regulations and policies are working well and which can be further improved for their efficacies. International experience shows that the systematic modeling and ecological accounting for production elements, flows and storages as well as a combination of regulations and financial instruments are most effective in reducing carbon emissions.
To achieve the carbon reductions goal as reinforced by the Copenhagen Accord, specific efforts on ecological accounting based on systems modeling and assessment will be implemented to obtain more fruitful results, which may help make corresponding energy policies to promote the energy efficiencies and reduce carbon emission intensity at each level of the concerned systems, to provoke financial incentives in the form of domestic and international investments for low carbon projects, and to expedite renewable energy and low-carbon technology innovations and applications to realize GHG regulation, and finally to approach real low-carbon economy.
Thereby, we would like to invite professionals from universities, enterprises, and administrative departments responsible for, involved in, or interested in low-carbon projects to make effective comparisons, present and share new ideas, innovations, trends, experiences, and concerns in the environmental accounting based on systems modeling and assessment. We also believe the cooperation of energy industries and researchers may establish an important platform ad attract more participants to exchange knowledge, perspectives and ideas for the low-carbon economy and to discuss the most recent advances in holistic evaluation models, simulation methods and improvements in low-carbon technologies from both theoretical and practical perspectives.
Dr. Bin Chen
Guest Editor
Submission
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Keywords
- low-carbon industrial park
- low-carbon building and real estate
- low-carbon evaluation and consultant
- multi-scale ecological input-output models
- renewable and substitute energy
- environmental emission accounting
- exergy analysis
- embodied energy analysis
- ecological network analysis
- carbon measurement
- low-carbon technologies
Published Papers (11 papers)
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Received: 30 May 2011; in revised form: 18 July 2011 / Accepted: 5 September 2011 / Published: 9 September 2011
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Abstract: China is experiencing a high speed economic development which may exert great pressure on the environment and energy systems. To measure the environmental and energy performance during the economic development process, this paper selected 30 provinces, cities or autonomous regions as the decision making unit (DMU), and proposed a Green GDP index (GGI) in view of energy intensity and pollution intensity using the generalized Data Envelopment Analysis (DEA) method, and the developing trends of integrated energy and environment efficiency of DMUs from 2006 to 2010 are also demonstrated by the Malmquist index. Results show that the integrated energy and environment efficiency varies for each DMU. GGI were both 1 in Beijing and Shanghai. GGI values for the developed cities in Eastern China, such as Guangdong, Fujian, Zhejiang, Tianjin, Jiangsu, and Hainan, ranked high, while those in the Northeast and Middle China remained relatively low. Moreover, there is a positive relationship between the GGI and per capita GDP with a correlation coefficient of 0.75. Increases in GGI are also observed in the results, representing great achievements are acquired in energy conservation and emission reduction. However, the GGIs do not converge to the green frontier across the provinces.
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Received: 13 June 2011; in revised form: 2 September 2011 / Accepted: 2 September 2011 / Published: 14 September 2011
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Abstract: How to design a production process with low carbon emissions and low environmental impact as well as high manufacturing performance is a key factor in the success of low-carbon production. It is important to address concerns about climate change for the large carbon emission source manufacturing industries because of their high energy consumption and environmental impact during the manufacturing stage of the production life cycle. In this paper, methodology for determining a production process is developed. This methodology integrates process determination from three different levels: new production processing, selected production processing and batch production processing. This approach is taken within a manufacturing enterprise based on prior research. The methodology is aimed at providing decision support for implementing Environmentally Benign Manufacturing (EBM) and low-carbon production to improve the environmental performance of the manufacturing industry. At the first level, a decision-making model for new production processes based on the Genetic Simulated Annealing Algorithm (GSAA) is presented. The decision-making model considers not only the traditional factors, such as time, quality and cost, but also energy and resource consumption and environmental impact, which are different from the traditional methods. At the second level, a methodology is developed based on an IPO (Input-Process-Output) model that integrates assessments of resource consumption and environmental impact in terms of a materials balance principle for batch production processes. At the third level, based on the above two levels, a method for determining production processes that focus on low-carbon production is developed based on case-based reasoning, expert systems and feature technology for designing the process flow of a new component. Through the above three levels, a method for determining the production process to identify, quantify, assess, and optimize the production process with the goal of reducing and ultimately minimizing the environmental impact while maximizing the resource efficiency is effectively presented. The feasibility of the method is verified by a case study of a whole production process design at the above three levels.
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Received: 11 July 2011; in revised form: 16 September 2011 / Accepted: 16 September 2011 / Published: 21 September 2011
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Abstract: The emergy concept, integrated with a multi-objective linear programming method, was used to model the agricultural structure of Xinjiang Uygur Autonomous Region under the consideration of the need to develop a low-carbon economy. The emergy indices before and after the structural optimization were evaluated. In the reconstructed model, the proportions of agriculture, forestry and artificial grassland should be adjusted from 19:2:1 to 5.2:1:2.5; the Emergy Yield Ratio (1.48) was higher than the average local (0.49) and national levels (0.27); and the Emergy Investment Ratio (11.1) was higher than the current structure (4.93) and that obtained from the 2003 data (0.055) in Xinjiang Uygur Autonomous Region, the Water Emergy Cost (0.055) should be reduced compared to that before the adjustment (0.088). The measurement of all the parameters validated the positive impact of the modeled agricultural structure. The self-sufficiency ratio of the system increased from the original level of 0.106 to 0.432, which indicated a better coupling effect among the subsystems within the whole system. The comparative advantage index between the two systems before and after optimization was approximately 2:1. When the mountain ecosystem service value was considered, excessive animal husbandry led to a 1.41 × 1010 RMB·a−1 indirect economic loss, which was 4.15 times the GDP during the same time period. The functional improvement of the modeled structure supports the plan to “construct a central oasis and protect the surrounding mountains and deserts” to develop a sustainable agricultural system. Conserved natural grassland can make a large contribution to the carbon storage; and therefore, it is wise alternative that promote a low-carbon economic development strategy.
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Received: 19 July 2011; in revised form: 1 September 2011 / Accepted: 5 September 2011 / Published: 21 October 2011
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Abstract: Energy is crucial in supporting people’s daily lives and the continual quest for human development. Due to the associated complexities and uncertainties, decision makers and planners are facing increased pressure to respond more effectively to a number of energy-related issues and conflicts, as well as GHG emission mitigation within the multiple scales of energy management systems (EMSs). This quandary requires a focused effort to resolve a wide range of issues related to EMSs, as well as the associated economic and environmental implications. Effective systems analysis approaches under uncertainty to successfully address interactions, complexities, uncertainties, and changing conditions associated with EMSs is desired, which require a systematic investigation of the current studies on energy systems. Systems analysis and optimization modeling for low-carbon energy systems planning with the consideration of GHG emission reduction under uncertainty is thus comprehensively reviewed in this paper. A number of related methodologies and applications related to: (a) optimization modeling of GHG emission mitigation; (b) optimization modeling of energy systems planning under uncertainty; and (c) model-based decision support tools are examined. Perspectives of effective management schemes are investigated, demonstrating many demanding areas for enhanced research efforts, which include issues of data availability and reliability, concerns in uncertainty, necessity of post-modeling analysis, and usefulness of development of simulation techniques.
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Received: 13 July 2011; in revised form: 11 October 2011 / Accepted: 12 October 2011 / Published: 21 October 2011
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Abstract: In view of the great contribution of coal-fired units to CO2 emissions, the coupled coal and power system with consideration of CO2 mitigation is a typical sub-system of the highly emitting Chinese energy system for low-carbon studies. In this study, an inexact mix-integer two-stage programming (IMITSP) model for the management of low-carbon energy systems was developed based on the integration of multiple inexact programming techniques. Uncertainties and complexities related to the carbon mitigation issues in the coupled coal and power system can be effectively reflected and dealt with in this model. An optimal CO2 mitigation strategy associated with stochastic power-generation demand under specific CO2 mitigation targets could be obtained. Dynamic analysis of capacity expansion, facility improvement, coal selection, as well as coal blending within a multi-period and multi-option context could be facilitated. The developed IMITSP model was applied to a semi-hypothetical case of long-term coupled management of coal and power within a low-carbon energy system in north China. The generated decision alternatives could help decision makers identify desired strategies related to coal production and allocation, CO2 emission mitigation, as well as facility capacity upgrade and expansion under various social-economic, ecological, environmental and system-reliability constraints. It could also provide interval solutions with a minimized system cost, a maximized system reliability and a maximized power-generation demand security. Moreover, the developed model could provide an in-depth insight into various CO2 mitigation technologies and the associated environmental and economic implications under a given reduction target. Tradeoffs among system costs, energy security and CO2 emission reduction could be analyzed.
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Received: 1 August 2011; in revised form: 8 October 2011 / Accepted: 31 October 2011 / Published: 4 November 2011
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Abstract: This paper considers the Global Thermodynamic Potential (GTP) indicator to perform a unified assessment of greenhouse gas (GHG) emissions, and to systematically reveal the emission embodiment in the production, consumption, and international trade of the Chinese economy in 2007 as the most recent year available with input-output table and updated inventory data. The results show that the estimated total direct GHG emissions by the Chinese economy in 2007 amount to 10,657.5 Mt CO2-eq by the GTPs with 40.6% from CH4 emissions in magnitude of the same importance as CO2 emissions. The five sectors of Electric Power/Steam and Hot Water Production and Supply, Smelting and Pressing of Ferrous and Nonferrous Metals, Nonmetal Mineral Products, Agriculture, and Coal Mining and Dressing, are responsible for 83.3% of the total GHG emissions with different emission structures. The demands of coal and coal-electricity determine the structure of emission embodiment to an essential extent. The Construction sector holds the top GHG emissions embodied in both domestic production and domestic consumption. The GHG emission embodied in gross capital formation is more than those in other components of final demand characterized by extensive investment and limited household consumption. China is a net exporter of embodied GHG emissions, with a remarkable share of direct emission induced by international trade, such as textile products, industrial raw materials, and primary machinery and equipment products exports. The fractions of CH4 in the component of embodied GHG emissions in the final demand are much greater than those fractions calculated by the Global Warming Potentials, which highlight the importance of CH4 emissions for the case of China and indicate the essential effect of CH4 emissions on global climate change. To understand the full context to achieve GHG emission mitigation, this study provides a new insight to address China’s GHG emissions status and hidden emission information induced by the final demand to the related policy-makers.
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Received: 31 October 2011; in revised form: 30 November 2011 / Accepted: 9 December 2011 / Published: 20 December 2011
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Abstract: The establishment of low-carbon cities has been suggested all over the World, since cities are key drivers of energy usage and the associated carbon emissions. This paper presents a scenario analysis of future energy consumption and carbon emissions for the city of Beijing. The Long-range Energy Alternatives Planning (LEAP) model is used to simulate a range of pathways and to analyze how these would change energy consumption and carbon emissions from 2007 to 2030. Three scenarios have been designed to describe future energy strategies in relation to the development of Beijing city, namely a reference scenario (RS), control scenario (CS), and integrated scenario (IS). The results show that under the IS the total energy demand in Beijing is expected to reach 88.61 million tonnes coal equivalent (Mtce) by 2030 (59.32 Mtce in 2007), 55.82% and 32.72% lower than the values under the RS and the CS, respectively. The total carbon emissions in 2030 under the IS, although higher than the 2007 level, will be 62.22% and 40.27% lower than under the RS and the CS, respectively, with emissions peaking in 2026 and declining afterwards. In terms of the potential for reduction of energy consumption and carbon emissions, the industrial sector will continue to act as the largest contributor under the IS and CS compared with the RS, while the building and transport sectors are identified as promising fields for achieving effective control of energy consumption and carbon emissions over the next two decades. The calculation results show that an integrated package of measures is the most effective in terms of energy savings and carbon emissions mitigation, although it also faces the largest challenge to achieve the related targets.
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Received: 20 October 2011; in revised form: 9 December 2011 / Accepted: 20 December 2011 / Published: 27 December 2011
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Abstract: China has achieved notable success in developing its economy with approximate 10 percent average annual GDP growth over the last two decades. At the same time, energy consumption and CO2 emissions almost doubled every five years, which led China to be the world top emitter in 2007. In response, China’s government has put forward a carbon mitigation target of 40%–45% reduction of CO2 emission intensity by 2020. To better understand the potential for success or failure of such a policy, it is essential to assess different driving forces such as population, lifestyle and technology and their associated CO2 emissions. This study confirms that increase of affluence has been the main driving force for China’s CO2 emissions since the late 1970s, which outweighs reductions achieved through technical progress. Meanwhile, the contribution of population growth to CO2 emissions was relatively small. We also found a huge disparity between urban and rural households in terms of changes of lifestyle and consumption patterns. Lifestyles in urban China are beginning to resemble Western lifestyles, and approaching their level of CO2 emissions. Therefore, in addition to the apparent inefficiencies in terms of production technologies there is also a lot of room for improvement on the consumption side especially in interaction of current infrastructure investments and future consumption.
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Received: 7 November 2011; in revised form: 30 January 2012 / Accepted: 30 January 2012 / Published: 13 February 2012
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Abstract: Threatened by the huge pressure caused by climate change, low-carbon cities have become an inevitable part of urban evolution. It is essential to evaluate urban low-carbon development levels to smoothly promote the construction of low-carbon cities. This paper proposes an evaluation index system for urban low-carbon development from the points of view of economic development and social progress, energy structure and usage efficiency, living consumption, and development surroundings. A weighted sum model was also established. Selecting 12 typical Chinese cities as cases studies, an integrated evaluation was conducted based on the index system and the assessment model. The development speed and limiting factors of different cities were also analyzed. The 12 cities were ultimately classified into three groups in terms of their low-carbon development patterns by integrating all of the analysis results. Furthermore, suitable regulation and management for different patterns were suggested. This study both aids in assessing the executive effect of low-carbon city construction and helps to determine existing problems and suggest effective solutions.
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Received: 8 December 2011; in revised form: 8 February 2012 / Accepted: 8 February 2012 / Published: 17 February 2012
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Abstract: Due to the movement and complexity of the carbon market, traditional monoscale forecasting approaches often fail to capture its nonstationary and nonlinear properties and accurately describe its moving tendencies. In this study, a multiscale ensemble forecasting model integrating empirical mode decomposition (EMD), genetic algorithm (GA) and artificial neural network (ANN) is proposed to forecast carbon price. Firstly, the proposed model uses EMD to decompose carbon price data into several intrinsic mode functions (IMFs) and one residue. Then, the IMFs and residue are composed into a high frequency component, a low frequency component and a trend component which have similar frequency characteristics, simple components and strong regularity using the fine-to-coarse reconstruction algorithm. Finally, those three components are predicted using an ANN trained by GA, i.e., a GAANN model, and the final forecasting results can be obtained by the sum of these three forecasting results. For verification and testing, two main carbon future prices with different maturity in the European Climate Exchange (ECX) are used to test the effectiveness of the proposed multiscale ensemble forecasting model. Empirical results obtained demonstrate that the proposed multiscale ensemble forecasting model can outperform the single random walk (RW), ARIMA, ANN and GAANN models without EMD preprocessing and the ensemble ARIMA model with EMD preprocessing.
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Received: 28 December 2011; in revised form: 13 February 2012 / Accepted: 14 February 2012 / Published: 22 February 2012
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Abstract: Great attention has been paid to carbon bio-sequestration due to increasing concerns over global warming. Understanding the relationship between carbon bio-sequestration and its influencing factors is of great significance for formulating appropriate management measures for global warming mitigation. Since change in carbon bio-sequestration is a complex process, it is difficult to take into account all of its influencing factors, while the panel data model may provide an effective way to measure their subtle effects. In this paper, decomposition analysis is applied to further analyze these influencing factors. The results indicate that climatic, demographic and geographical variables play important roles in explaining the spatial heterogeneity of carbon bio-sequestration in China, which is consistent with previous researches. Meanwhile, the irrigation rate is found to be the most critical factor influencing carbon bio-sequestration changes, followed by climatic and economic factors. These results may provide decision makers in China with important scientific reference information for formulating regional carbon bio-sequestration management policies, which are of great significance to alleviating and adapting to global warming.
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Last update: 26 April 2011
