资源型区域碳排放影响因素分析——以山西省为例

碳排放问题是应对气候变化的主要问题,更是困扰资源型区域改革与可持续发展的关键性问题。以山西省为例,采用LMDI分解方法,将碳排放分解为5种影响因素,对山西省碳排放趋势和变化、影响因素做了深入探讨。结果表明:1)山西省未来碳排放增速减缓,排放量将持续增加,生态环境压力进一步加大;2)能源结构效应为负向影响,虽效应值较小,但潜力巨大。而人口规模正向效应值缓慢变大需要关注;3)在产业结构效应方面,工业、交通运输仓储邮政业及批发零售住宿餐饮业部门产业效应为正效应,且有逐年增大的趋势。表明第二产业和第三产业内部结构不合理,产业结构优化有待提高。     

我国城乡居民消费差异对CO2的影响分析

采用相关年度数据,利用脉冲响应函数和方差分解技术,详细分析了我国城乡居民消费现状,并探究了我国城乡居民消费差异对CO2排放的影响。结果表明,我国城镇居民的消费结构已由生存型消费向发展享受型转变,但农村居民仍旧停留在以"吃穿用"为主的传统消费阶段,这使得城乡居民消费水平的提高对CO2排放存在巨大差异。据测算,城镇居民消费水平的提高对CO2排放的影响几乎是农村居民消费水平提高对其影响的10倍。鉴于此,我国实现节能减排的目标,不仅需要改变生产方式,更需要转变居民消费理念、调整消费结构、引导居民合理消费,使居民消费行为由高能耗消费向低能耗消费转变。     

京、津、冀地区的碳排放趋势估计

通过多种方法相结合,估计了京、津、冀地区2009-2050年的能源碳排放量、水泥工艺碳排放量和森林碳汇量,计算了区域总的碳排放量和净碳排放量。结果表明:1)京、津、冀地区碳排放量都呈现先升后降的Kuznets趋势,森林年碳汇量对碳排放降低影响不明显;2)在自由排放条件下,北京、天津均在2030年前达到碳排放高峰期,河北省及整个京津冀地区的碳排放高峰将延迟到2039年;3)在2050年前,北京、天津年碳汇量将有所降低,河北年碳汇量则上升。     

我国城市居民直接能耗的碳排放类型及影响因素

针对我国287个地级以上城市,在测算了近9年居民直接能耗导致的CO₂排放量的基础上,进行聚类、对比,并分析城市居民直接能耗的碳排放影响因素,得到以下结论:全国分为6类城市居民直接能耗碳排放类型;高碳排放型城市的地均碳排放强度、人均工资碳排放强度及居民直接能耗CO₂排放总量等方面均比低碳排放型城市高,人均地方生产总值碳排放强度低于低碳排放型城市,并多为经济发达城市和资源丰富城市,其碳排放构成上分别以电、交通能耗碳排放和气碳排放为主导,高碳排放型城市居民直接能耗CO₂排放量占全国地级以上城市的86.20%。我国大部分地级城市居民直接能耗的碳排放属于相对低碳排放型,其人均CO₂排放量低于全国平均水平。城市所在地的降温度日数(CDD)、采暖期、采暖强度、人均能源供给量、居民的人均工资、城市人均地方生产总值是影响城市居民直接能耗CO₂排放量的主要因素。     

Growing up and cleaning up: The environmental Kuznets curve redux

Borrowing from the Kuznets curve literature, researchers have coined the term “environmental Kuznets curve” or EKC to characterize the relationship between pollution levels and income: pollution levels will increase with income but some threshold of income will eventually be reached, beyond which pollution levels will decrease. The link between the original Kuznets curve, which posited a similar relationship between income and inequality, and its pollution-concerned offspring lies primarily with the shape of both curves (an upside-down U) and the central role played by income change. Although the EKC literature has burgeoned over the past several years, few concrete conclusions have been drawn, the main themes of the literature have remained constant, and no consensus has been reached regarding the existence of an environmental Kuznets curve. EKC research has used a variety of types of data and a range of geographical units to examine the effects of income levels on pollution. Changes in pollution levels might also be at least partly explained by countries’ position in the demographic transition and their general population structure, however little research has included this important aspect in the analysis. In addition, few analyses confine themselves to an evaluation for one country of the long-term relationship between income and pollution. Using United States CO₂ emissions as well as demographic, employment, trade and energy price data, this paper seeks to highlight the potential impact of population and economic structure in explaining the relationship between income and pollution levels.     

Estimation of the Distribution of Global Anthropogenic Heat Flux

The radiance lights data in 2006 from the National Oceanic and Atmospheric Administration Air Force Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and authoritative energy data distributed by the United State Energy Information Administration were applied to estimate the global distribution of anthropogenic heat flux.A strong linear relationship was found to exist between the anthropogenic heat flux and the DMSP/OLS radiance data.On a global scale,the average value of anthropogenic heat flux is approximately 0.03 W m 2 and 0.10 W m 2 for global land area.The results indicate that global anthropogenic heat flux was geographically concentrated and distributed,fundamentally correlating to the economical activities.The anthropogenic heat flux concentrated in the economically developed areas including East Asia,Europe,and eastern North America.The anthropogenic heat flux in the concentrated regions,including the northeastern United States,Central Europe,United Kingdom,Japan,India,and East and South China is much larger than global average level,reaching a large enough value that could affect regional climate.In the center of the concentrated area,the anthropogenic heat flux density may exceed 100 W m 2,according to the results of the model.In developing areas,including South America,Central and North China,India,East Europe,and Middle East,the anthropogenic heat flux can reach a level of more than 10 W m 2 ;however,the anthropogenic heat flux in a vast area,including Africa,Central and North Asia,and South America,is low.With the development of global economy and urban agglomerations,the effect on climate of anthropogenic heat is essential for the research of climate change.     

Impacts of Future NOx and CO Emissions on Regional Chemistry and Climate over Eastern China

A coupled chemical/dynamical model (SOCOL-SOlar Climate Ozone Links) is applied to study the impacts of future enhanced CO and NOx emissions over eastern China on regional chemistry and climate. The result shows that the increase of CO and NOx emissions has significant effects on regional chemistry, including NOx, CO, O₃, and OH concentrations. During winter, the CO concentration is uniformly increased in the northern hemisphere by about 10 ppbv. During summer, the increase of CO has a regional distribution. The change in O₃, concentrations near eastern China has both strong seasonal and spatial variations. During winter, the surface O₃, concentrations decrease by about 2 ppbv, while during summer they increase by about 2 ppbv in eastern China. The changes of CO, NOx, and O₃, induce important impacts on OH concentrations. The changes in chemistry, especially O₃, induce important effects on regional climate. The analysis suggests that during winter, the surface temperature decreases and air pressure increases in central-eastern China. The changes of temperature and pressure produce decreases in vertical velocity. We should mention that the model resolution is coarse, and the calculated concentrations are generally underestimated when they are compared to measured results. However, because this model is a coupled dynamical/chemical model, it can provide some useful insights regarding the climate impacts due to changes in air pollutant emissions.     

以科学发展观为指导，积极应对气候变化

气候变化已经对全球生态、环境、经济和社会可持续发展以及外交和国家安全产生极大影响,引起各国政府、公众和学术界的极大关注。气候变化关系人类的生存和发展,应对气候变化实现可持续发展具有重要性和紧迫性。我们要以科学发展观为指导,把握好共同但有区别的责任,积极应对气候变化。     

珞珈一号夜间灯光数据的福建省人为热通量估算

夜间灯光数据是估算人为热通量（AHF）的重要数据,但当前应用最广的DMSP/OLS和Suomi-NPP/VIIRS夜间灯光数据由于受限于粗糙的空间分辨率,而无法刻画城市内部的AHF分布细节。中国2018年6月发射的Luojia 1-01卫星所获取的130 m高空间分辨率夜间灯光数据,则有望解决这一问题。因此本文利用Luojia 1-01夜间灯光数据,通过将统计年鉴中的能源统计数据细化至福建省84个县（市、区）,然后与3个夜间灯光指数（NTL_nor、HSI、VANUI）进行回归分析,分别构建了基于这3个指数的福建省AHF空间估算模型,并采用交叉验证法对其进行筛选。结果显示：（1）在3个指数中,基于VANUI的乘幂估算模型的R²最高,且RMSE最小,因此精度最高;（2）利用VANUI乘幂估算模型反演得到的2018年福建省年均AHF为0.88 W/m²,其中厦门市的年均AHF最高,达10.98 W/m²,泉州、莆田、福州、漳州等沿海城市次之,年均值在0.98—1.95 W/m²,而宁德、龙岩、三明、南平等城市的AHF则较低,均值在0.38—0.46 W/m²;（3）Luojia 1-01夜间灯光数据可以揭示城市内部的AHF分异细节。根据用地属性和功能的不同,AHF数值表现为：城市集中商业区>大型市政公共设施区>城市主干道>城市住宅区>近郊住宅区。研究表明,基于Luojia 1-01夜间灯光数据建立的AHF估算模型可以较好地揭示城市尺度AHF的空间分异情况。     

基于倾向得分匹配方法的中国自然保护区缓解人类活动压力评估

科学地评估自然保护区缓解人类活动压力的效果,对于有效的自然保护至关重要。目前中国国家尺度下的这方面研究,仅将保护区内、外的人类活动压力进行对比,其缺陷是样本选择性偏差会导致评估结果的不合理。本文选择了倾向得分匹配（Propensity Score Matching）方法来克服保护区评价中的样本选择性偏差问题。首先耦合多源数据构建了2013年、2015年、2017年的人类活动压力指数,然后采用倾向得分匹配方法对自然保护区内、外的随机点进行匹配,使两个对比组的观测变量尽可能相似。最后通过相对有效性指标和面板模型从不同层面评估了中国680个自然保护区在2013—2017年间缓解人类活动压力的效果。研究结果表明：① 2013—2017年,全国86.72%的自然保护区内人类活动压力指数呈现上升趋势,其面积占保护区总面积的43.80%。② 69.85%的自然保护区在缓解人类活动压力方面的效果较好。其中,除了海洋海岸、野生植物、野生动物类的保护区以外,其余保护区类型都表现出较好的缓解人类活动压力的效果,且保护区级别越高,保护效果越好。③ 中国自然保护区建设在2013—2017年间能缓解22.90%的人类活动压力,且保护区缓解人类活动压力的能力存在区域性差别。本文研究结果可为中国自然保护区监测、评估和管理提供更科学的参考依据。