Energy efficiency and appliance’s characteristics considered prior to purchase: Differences and similarities between the United Kingdom,Germany, Portugal,Greece, Poland,Spain and Italy

This article reports country differences in the consumer’s most considered characteristics when choosing electrical appliances, including but not restricted to the energy efficiency aspect. A survey was performed to store customers from 7 countries: the United Kingdom; Germany; Portugal; Greece; Poland; Spain; Italy. Results showed consistency between countries in the top three characteristics considered: cost; quality; and a balance between price and quality. Differences were found for reported environmental attitudes and behaviours, purchase motives, and store employees evaluation. The results may support national policies and store level energy efficiency interventions. Specifically, they can provide input for store employee’s training, in persuading customers towards the purchase of energy efficient appliances.     

Experimental investigation of a 250-kW turbine organic Rankine cycle system for low-grade waste heat recovery

An organic Rankine cycle (ORC) is generally used for converting low-grade heat into electricity. In this study, an extensive literature survey was conducted to identify current research gaps on experimental ORC systems. Specifically, there is limited experimental data and limited details on thermal and expander efficiencies of ORC systems. In order to address these gaps, the objective of this study included developing a turbine ORC with a power output exceeding 50 kW and thermal efficiency exceeding 8% for a heat source temperature < 120°C. The experimental results indicated that the system achieved a net power output of 242.5 kW and a thermal efficiency of 8.3% (the highest value for a turbine ORC system for the heat source temperature below 120°C). Thus, the study addressed the gaps identified in the research area of ORCs.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Modeling urban solar energy with high spatiotemporal resolution: A case study in Toronto,Canada

This research presents a method to determine the maximum potential for the capturing of solar radiation on the rooftop of buildings in an urban environment. This involves the modeling of solar energy potential and comparison to historical building energy demand profiles through the use of 3-D solar simulation software tools and geographic information systems (GIS). The objective is to accurately identify the amount of surface area that is suitable for solar photovoltaic (PV) installations and to estimate the hourly PV electricity generation potential of existing building rooftops in an urban environment. This study demonstrates a viable approach for modeling urban solar energy and offers valuable information for electricity distributors, policy makers, and urban energy planners to facilitate the substantial design of a green built environment. The developed methodology is comprised of three main sections: (1) determination of suitable rooftop area, (2) determination of the amount of incident solar radiation available per rooftop, and (3) estimation of hourly solar PV electricity generation potential. A case study was performed using this method for Ryerson University, located in Toronto, Canada. It was found that solar PV could supply up to 19% of the study area’s electricity demands during peak consumption hours. The potential benefits of solar PV was also estimated based upon hourly greenhouse gas emission intensity factors as well as Time-of-Use (TOU) savings through the Ontario Feed-in-Tariff (FIT) program, which allows for better representation of the positive impacts of solar technologies.     

The effect of industry activities on public support for ‘fracking’

Research suggests that previous, current, and prospective extractive industry activities influence perceptions of new development. Studies that have drawn this conclusion, however, have usually focused on specific projects in specific communities. Here, these factors are examined on an aggregate, national scale. Combining geospatial data on extractive industry activities and survey data from a nationally representative sample (N = 1061), the influence of extractive industry activities on support for fracking is studied. While limited evidence is found for the impact of proximity to oil and gas wells or production on support for fracking, employment levels in the natural resources and mining sector in the respondent’s county and residence in an area experiencing active oil and gas development significantly increase support for fracking. The results highlight the role of spatial and community factors in shaping support for energy development.     

Moving targets: Carbon pricing,energy markets,and social movements in Australia

Reporting on the origins and directions of social movement strategy on climate and energy issues in the last decade, the shifts in ‘climate movement’ practice are discussed using a neo-Polanyian account of the political economy of climate change combined with sociological analysis of the strategic decisions campaigners reported making. Since the mid-2000s, Australia’s climate movement has been engaged in three concurrent arenas of political contestation. The longest-standing arena of movement activity has been negotiations over climate policy. More recently, activists and communities are engaged in a struggle over the expansion of fossil fuels. A third contest has been waged over the present and future position of renewable energy technologies in Australia’s electricity market. In the wake of climate policy failure, energy campaigns have been deepened, and it seems that a broader energy justice agenda is being forged. New strategic dilemmas are visible in the field.     

家庭部门生活能源消费碳排放:测度与驱动因素研究

本文运用IPCC的二氧化碳排放量测算方法,在省际层面测度了我国家庭部门直接能源消费碳排放,并基于扩展的STIRPAT和Kaya模型,构建家庭部门直接能源消费碳排放影响因子动态面板数据模型,对我国2003-2012年分省面板数据样本及城乡子样本进行系统GMM估计。本文研究表明,第一,我国城乡家庭部门碳排放总量和人均碳排放在近十年都呈快速上升趋势,家庭部门碳排放的空间分布具有明显的地域差异特征,高碳排放地区主要集中在东、中部地区,西部地区的碳排放水平较低;第二,城乡家庭部门生活能源消费的上期碳排放量对本期碳排放产生重要的正向影响,这反映出我国家庭部门碳排放具有显著的惯性特征和路径依赖性,是一种动态自适应机制;人口规模、居民消费水平、能源消费结构、碳排放强度、能源消费强度和城镇化因素,都对我国居民能源消费碳排放总量及人均碳排放具有显著的影响,城乡之间的家庭能源消费碳排放驱动因素存在差异。本文研究得到如下启示及政策含义:家庭部门碳减排将是一个有步骤、分区域的渐进过程,我国碳减排政策应当兼顾消费升级和碳排放的双重目标,努力构建分层次碳减排的适应性预期机制。具体而言,一方面应着眼于引导和激励居民低碳消费,缓解人口规模增加、消费水平提升和城镇化对家庭部门碳减排的压力;另一方面要通过能源价格改革、财政政策和环境规制政策等优化我国能源消费结构,不断降低煤炭消费比重,提高清洁能源的消费。同时,通过技术创新、设备改造等科技手段提高煤炭利用效率,降低碳排放强度,这些政策将更有利于城镇家庭部门碳减排。     

城镇化与工业化对能源强度影响的实证研究——基于截面相关和异质性回归系数的非平衡面板数据模型

城镇化、工业化对中国能源强度的影响如何?如何在快速推进城镇化、工业化进程的同时确保节能减排目标的实现?论文以能源强度指标代替传统的能源消费指标来反映能源综合利用效率,并应用考虑截面相关性和异质性回归系数的非平衡面板数据模型,使用共同相关效应组均值(CCEMG)估计方法对中国1978-2014年城镇化、工业化与能源强度之间的关系进行分析。研究结果表明:人均实际GDP增长1%,能源强度将会降低0.412%,工业化水平增长1%,能源强度将会上升0.630%,而由于生产消费等经济活动的增加、高度集中化以及规模经济的综合作用,使得城镇化对能源强度的影响并不确定。联系研究结论,本文提出政策建议:我国应加快产业结构升级,转变经济增长方式;构建绿色制造体系,推进"五化"协同发展;推进绿色、循环、低碳发展的新型城镇化建设,提高城镇化质量,提升我国整体的能源效率,确保节能减排目标的实现,推动经济全面、协调、可持续发展。     

中国区域工业企业绿色技术创新效率及因素分解

本文基于两阶段创新价值链下工业企业绿色技术创新的过程,构建了中国区域工业企业绿色技术创新效率评价体系,同时运用主成分分析法对这些指标数据进行降维处理,分别计算出两个阶段的技术创新效率,再通过DEA法测算各区域工业企业整体绿色技术创新效率并进行因素分解。研究发现:1在绿色技术开发阶段,考虑环境因素比不考虑环境因素的效率要低,中、西部地区面临的环境问题更加严峻,三大区域绿色技术开发效率差距悬殊。2在绿色技术成果转化阶段,绿色技术创新效率仍存在较大提升空间,东、中和西部地区绿色技术创新效率从高到低依次排列。3中国工业企业绿色技术创新整体效率偏低,而纯技术效率是导致整体效率低的主要原因。区域差距悬殊且存在继续扩大的风险,中、西部地区在纯技术效率和规模效率两个方面均有很大提升空间。4辽宁、河北、黑龙江、新疆、山西、内蒙古、云南、青海等区域要注重提高区域技术,安徽、吉林、江西、广西等区域则要注重减少资源冗余,宁夏、甘肃则既要注重提高区域技术还要注意较少资源冗余。未来,中国应该建立低碳经济发展模式,将环境污染指标工业企业技术创新效率评价体系,提升绿色技术创新效率。以供给侧改革为突破口,加快区域协同发展,发挥东部地区引领作用,缩小中国东、中、西部地区差异。因地制宜,根据各省份绿色技术创新效率不高的根源,采取有针对性的政策。加大绿色技术开发、清洁生产设备等方面的投入,控制污染物排放,推动以科技创新为核心的全面创新。     

中国碳市场波动溢出效应研究

自2013年以来,中国已先后建立了七个区域性碳市场。研究中国碳市场之间的波动溢出效应,有助于发现在碳市场运行初期起价格引导作用的区域市场,为其它地区碳市场的发展和全国碳市场的建立提供建议。本文选择广东、湖北和深圳三个交易量最大的区域碳市场为样本,利用多元GARCH(1,1)-BEKK模型,检验其波动溢出效应。为了消除履约期的影响,本文根据履约期将样本划分为阶段一(2014年7月1日—2015年6月30日)和阶段二(2015年7月1日—2016年7月19日),分阶段进行了检验,并从市场有效性的视角解释了检验结果。波动溢出效应检验结果显示:在阶段一内,广东碳市场对湖北碳市场、湖北碳市场对深圳碳市场、深圳碳市场对广东碳市场分别存在单向的波动溢出效应;在阶段二内,只存在深圳碳市场对广东碳市场的单向波动溢出效应;在整个样本期内,只存在深圳碳市场对广东碳市场的单向波动溢出效应。基于方差比的市场有效性检验结果显示:无论是阶段一、阶段二或整个样本期,三个碳市场均没有达到弱式有效。本文进一步在多重分形检验的基础上,利用多重分形谱宽度,比较了三个碳市场的非有效性程度,结果显示:在阶段一、阶段二和整个样本期,三个碳市场有效性大小关系分别为:湖北广东深圳、深圳湖北广东、广东深圳湖北。可见,三个碳市场早期的波动溢出效应不完全符合市场有效性大小关系,而较晚期的波动溢出效应符合市场有效性大小关系,这证明了中国碳市场在阶段二相比阶段一更为成熟。本文最后对中国碳排放权交易市场的发展提出了政策建议。