Intergenerational transfers in US county-level CO2 emissions, 2007

Research has shown that increases in carbon emissions and resulting climate change are not driven by population size alone, but also associated with industrialization, urbanization, and economic development. Further, industrialization and development may, in part, be driven by changing demographic structure, and in particular the process of population aging. Fluctuations in age composition shape aggregate production and consumption. Viewed through this lens, the carbon dioxide emissions of an analytical unit (county, province, state, nation) can be considered a product of its age composition. This analysis tests several demographic theories of age-specific production and consumption on US county-level carbon dioxide emissions. Using a modified STIRPAT framework, econometric estimates identify a positive correlation between county-level labor force participation and total carbon dioxide emissions. These effects are a result of general economic activity as opposed to growth only in energy intensive industrial sectors, a relationship that is widely hypothesized but under-developed in carbon emission estimates. In addition, results show larger households are associated with lower aggregate emissions, confirming the hypothesis that areas of declining household size will experience higher future emissions. In general, this research demonstrates the importance of adding nuance to emission estimates by integrating demographic dimensions beyond population size and growth.     

The Leverage of Demographic Dynamics on Carbon Dioxide Emissions: Does Age Structure Matter?

This article provides a methodological contribution to the study of the effect of changes in population age structure on carbon dioxide (CO₂) emissions. First, I propose a generalization of the IPAT equation to a multisector economy with an age-structured population and discuss the insights that can be obtained in the context of stable population theory. Second, I suggest a statistical model of household consumption as a function of household size and age structure to quantitatively evaluate the extent of economies of scale in consumption of energy-intensive goods, and to estimate age-specific profiles of consumption of energy-intensive goods and of CO₂ emissions. Third, I offer an illustration of the methodologies using data for the United States. The analysis shows that per-capita CO₂ emissions increase with age until the individual is in his or her 60s, and then emissions tend to decrease. Holding everything else constant, the expected change in U.S. population age distribution during the next four decades is likely to have a small, but noticeable, positive impact on CO₂ emissions.     

基于非线性假设的人口和碳排放关系研究

理论和实证分析表明,人口和碳排放的关系具有动态性,碳排放量对人口要素的弹性系数是人口数量、年龄结构、家庭规模、城市化、性别结构、经济发展水平等因素的非线性函数。本文发现,基于人口和碳排放非线性关系假设构建的碳排放模型对历史数据的拟合能力明显优于传统模型,能够较好地揭示不同人口发展阶段和经济发展水平下的人口动态对于碳排放的影响。从各国人口发展实际状况出发,制定相应的减排策略是应对气候变化的必然选择。     

人口结构、城镇化与碳排放——基于跨国面板数据的实证研究

文章基于美国、中国、日本、英国等9国1961～2010年的面板数据对人口年龄结构、城镇化与碳排放之间的关系进行实证分析。结果表明,人口规模、人均GDP、二氧化碳排放强度、化石能源占能源消费总量的比重及人口结构与全球碳排放显著相关。此外,人口城镇化率与碳排放的关系呈倒U形,即在人口城镇化的早期会促进二氧化碳的排放,但随着城镇化的进一步扩大则会抑制碳排放;而人口的年龄结构,尤其是人口的老龄化程度对碳排放量的影响则具有U形的特点,即在人口老化的初期由于老年人群的消费模式会减少碳排放,但当人口老龄化进一步加剧后,老年人对医疗护理等方面的需求增多,以致需要更多的经济活动支持这部分开支,因而会造成碳排放的增加。     

我国家庭动态变化对二氧化碳排放的影响分析

人口数量及其增长率常常被认为是二氧化碳排放增长的重要推动力之一,而家庭变动对二氧化碳排放的影响往往被忽视。基于此,采用VAR模型,通过脉冲响应函数来考察人口和家庭变动对二氧化碳排放的动态影响,并用方差分解法揭示其相互影响程度及差异。结果表明,家庭层面变量对二氧化碳排放的影响远大于人口总量对二氧化碳排放的影响。考察人口总量、平均家庭规模以及家庭户变动三者对二氧化碳排放影响的贡献差异发现,平均家庭规模对二氧化碳排放的影响大于家庭户变动对二氧化碳排放的影响,且大于人口总量对二氧化碳排放的影响。相对于人口总量,以家庭户为视角来研究人口因素对二氧化碳的影响更为重要。因此,在节能减排的政策建议中,应当更多倡导推广有利于可持续发展的家庭户模式,家庭减排对于减排目标的实现将具有更大的潜力。     

Age-structure,urbanization, and climate change in developed countries: revisiting STIRPAT for disaggregated population and consumption-related environmental impacts

We focus on three environmental impacts particularly influenced by population age-structure—carbon emissions from transport and residential energy and electricity consumption—as well as aggregate carbon emissions for a panel of developed countries, and take as our starting point the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework. Among our contributions is to further disaggregate population into three particularly key age groups: 20–34, 35–49, and 50–64, and by doing so demonstrate that population’s environmental impact differs considerably across age groups, with the older age groups (ones typically associated with larger households) actually exerting a negative influence. Furthermore, those age-specific population influences are different (in absolute and relative terms) for the different environmental impacts we analyze. Also, we find that urbanization, in developed countries, best measures access to a country’s power grid, and thus, is positively associated with energy consumption in the residential sector. Finally, we suggest some modeling and methodological improvements to the STIRPAT framework.     

日本工业化进程中人口因素对碳排放影响研究

近年来发达国家的统计数据显示,居民生活消费的直接与间接能源消耗已超过产业部门,成为碳排放的主要增长点。运用STIRPAT模型,研究日本工业化进程中人口因素对碳排放的影响,从人口与消费视角探讨碳排放问题,将对我国工业化阶段的碳减排起到一定的指导作用。提高低碳技术水平、控制人口规模、引导居民消费模式的合理转变将成为控制工业化进程中碳排放的有效手段。     

Examining the Impact of Demographic Factors on Air Pollution

This study adds to the emerging literature examining empirically the link between population size, other demographic factors and pollution. We contribute by using more reliable estimation techniques and examine two air pollutants. By considering sulfur dioxide, we become the first study to explicitly examine the impact of demographic factors on a pollutant other than carbon dioxide at the cross-national level. We also take into account the urbanization rate and the average household size neglected by many prior cross-national econometric studies. For carbon dioxide emissions we find evidence that population increases are matched by proportional increases in emissions while a higher urbanization rate and lower average household size increase emissions. For sulfur dioxide emissions, we find a U-shaped relationship, with the population-emissions elasticity rising at higher population levels. Urbanization and average household size are not found to be significant determinants of sulfur dioxide emissions. For both pollutants, our results suggest that an increasing share of global emissions will be accounted for by developing countries. Implications for the environmental Kuznets curve literature are described and directions for further work identified.     

How do Recent Population Trends Matter to Climate Change?

Although integrated assessment models (IAM) of the Intergovernmental Panel on Climate Change (IPCC) consider population as one of the root causes of greenhouse gas emissions, how population dynamics affect climate change is still under debate. Population is rarely mentioned in policy debates on climate change. Studies in the past decade have added significantly to understanding the mechanisms and complexity of population and climate interactions. In addition to the growth of total population size, research shows that changes in population composition (i.e. age, urban–rural residence, and household structure) generate substantial effects on the climate system. Moreover, studies by the impact, vulnerability and adaptation (IAV) community also reveal that population dynamics are critical in the near term for building climate change resilience and within adaptation strategies. This paper explores how global population dynamics affect carbon emissions and climate systems, how recent demographic trends matter to worldwide efforts to adapt to climate change, and how population policies could make differences for climate change mitigation and adaptation.     

家庭人口老化对碳排放的影响——基于家庭微观视角的实证研究

人口老龄化和全球气候变暖已经成为世界各国共同关注的议题。在我国人口老龄化日益加剧、环境问题也逐渐引起社会各界广泛关注的背景下,家庭层面人口老化与碳排放之间关系值得深入研究。文章从微观家庭结构出发,对家庭内部人口老化与碳排放之间关系进行理论分析,利用中国家庭金融调查数据库(CHFS)2013年的截面数据对人口老化与碳排放的关系进行实证研究。研究结果表明家庭的老化特征与家庭碳排放之间呈现负相关,家庭的老化特征有助于减轻家庭的碳排放水平,老年人与年轻人共同生活的主干家庭更加"节能环保",这一结论与人们的一般感性认识有所不同。因此,应当鼓励年轻人与老年人共同居住,通过老年人的生活方式和消费习惯影响家庭的消费行为和消费结构,使家庭朝着节约环保型转变。本文的结论同时也表明人口老龄化产生的并非都是消极影响,其对社会的影响需要进行全面评估以积极应对老龄社会的到来。     

Household dynamics and fuelwood consumption in developing countries: a cross-national analysis

Previous research has suggested a link between household dynamics (i.e., average household size and number of households) and environmental impacts at the national level. Building on this work, we empirically test the relationship between household dynamics and fuelwood consumption, which has been implicated in anthropogenic threats to biodiversity. We focus our analysis on developing countries (where fuelwood is an important energy source). Our results show that nations with smaller average households consume more fuelwood per capita. This finding indicates that the household economies of scale are, indeed, associated with the consumption of fuelwood. In addition, we found that number of households is a better predictor of total fuelwood consumption than average household size suggesting a greater relative contribution to consumption levels. Thus, insofar as declining average household sizes result in increased number of households and higher per capita consumption, this trend may be a signal of serious threats to biodiversity and resource conservation. We also found further support for the ??energy ladder?? hypothesis that economic development reduces demand for traditional fuels.     

我国人口态势与消费模式对碳排放的影响分析

本文应用STIRPAT扩展模型,考察近30年来我国人口规模、人口结构、居民消费及技术进步因素对碳排放的影响。研究发现,居民消费与人口结构变化对我国碳排放的影响已超过人口规模的单一影响力。居民消费水平提高与碳排放增长高度相关,居民消费模式变化正在成为我国碳排放的新的增长点;人口结构因素中,人口城镇化率的提高通过对化石能源消费、水泥制造及土地利用变化等的影响导致碳排放增长;人口年龄结构变化对生产的影响大于对消费的影响,其对碳排放影响的主要途径是生产领域劳动力的丰富供应;家庭户规模减小导致人均消费支出的增加及总户数消费规模的扩张,以家庭户为分析单位考察其对碳排放的影响具有较高的解释力。针对分析结果,探讨了未来我国低碳社会发展的相关应对之策。     

人口因素对CO2排放的影响——基于面板分位数回归的实证研究

基于1995～2009年中国省际面板数据,利用面板分位数回归模型估计人口因素对我国CO2排放量的影响,结果显示:人口数量和人口城市化率是影响我国CO2排放的主要人口因素,但从影响大小上看,人口数量变化对发达省份CO2排放的影响大于欠发达省份,而人口城市化率则对欠发达省份的CO2排放具有更大的影响;家庭小型化对CO2排放的影响因省而异,对不同省份,要么没有明显的影响,要么可能导致CO2排放量增加;年龄结构目前还不是导致我国CO2排放量变化的主要人口因素;综合比较而言,经济发展水平对CO2排放的影响大于人口各因素,产业结构对CO2排放的影响小于人口数量和人口城市化率,而技术进步与CO2排放的关系则显得模糊。     

人口与消费对二氧化碳排放的动态影响——基于变参数模型的实证分析

近年来,随着全球气候的不断变暖,二氧化碳减排问题已经引起了世界性的关注。中国作为世界上最大的碳排放国,必须逐步降低经济发展过程中的碳排放。基于此,利用1978～2009年的相关数据和变参数模型,分析了人口数量和居民消费对我国二氧化碳排放的动态影响。结果表明,人口、消费与二氧化碳排放之间存在长期稳定的关系,二者对碳排放均有比较显著的影响。从总体来看,人口对碳排放的影响弹性要高于消费对碳排放的弹性,但是近年来两者之间的差距越来越小,说明消费对二氧化碳排放的影响力日益增大;随着时间波动,人口与消费对碳排放的影响弹性会呈现规律性的变化,并且可以将其划分为三个明显的阶段。依据上述结论,文章提出了相关政策建议,以减少由人口和消费带来的二氧化碳排放。     

How does population growth contribute to rising energy consumption in America?

The contribution of American population growth to rising energy consumption is analyzed for the period 1947–91. Energy consumption is disaggregated into electricity and nonelectricity consumption, and by end-use sectors: residential and commercial, industrial, and transportation. Population growth has been relatively unimportant as a contributor to yearly fluctuations in energy consumption. However, whereas energy changes induced by nonpopulation factors are erratic, sometimes adding consumption and sometimes subtracting, population growth consistently adds to consumption. As a result, depending upon which energy sector is considered, population growth may have a dominant role in the longterm growth of consumption.     

京津冀人口集聚对能源消费的影响

京津冀作为我国重要的人口集聚区,其区域发展面临严重的能源和环境问题。首先,基于STIRPAT模型构建京津冀面板数据模型,研究1990—2017年京津冀人口密度、经济增长、产业结构和环境规制对能源消费的影响。其次,采用SVAR模型通过脉冲响应函数和方差分解,研究京津冀人口密度变动对能源消费的作用机理和动态影响关系。结果显示,京津冀三地的人口密度、人均实际GDP与能源消费之间存在正向影响关系,其中河北省影响系数最大;北京和天津第三产业占比与能源消费之间存在反向影响关系,河北省第三产业占比对能源消费的影响不显著;北京和天津的环境规制对能源消费影响不显著,河北省环境规制对能源消费具有正向影响;京津冀地区和京冀两地人口密度变动对能源消费的影响在短期呈现负向效应,但是在中长期京津冀地区和三地人口密度变动对能源消费的影响均先上升然后缓慢下降,人口集聚具有集约用能效应。相对于能源消费自身影响而言,京津冀人口密度变动对能源消费的贡献相对较小,其影响效应有限。最后,对京津冀人口流动和能源消费一体化协同发展提出对策建议。     

家庭教育支出决定因素分析--来自中国城镇住户调查数据的经验证据

利用北京、辽宁、浙江、广东、四川与陕西2002年和2009年的城镇住户调查数据对家庭教育支出行为的决定因素和家庭教育支出的影响机制进行了实证检验与扩展分析。结论表明；（1）2002年家庭教育总支出的收入弹性大于2009年的家庭教育总支出收入弹性，且2002年的收入弹性大于1；家庭教育支出行为与户主年龄呈现显著的“U”型关系，且女孩比男孩获得更多的家庭教育资源；（2）各分项教育支出中，培训费支出受家庭可支配收入的影响最大；（3）城市人口规模与家庭教育支出之间呈现正相关关系；（4）总体而言，妻子对家庭教育支出的影响显著大于丈夫的影响，这主要体现在受教育年限方面。     

人口增长、城市化对环境的影响——以CO2排放为例

基于1978—2007中国宏观经济时间序列数据,利用协整回归方法和误差修正模型(ECM),实证分析了人口增长、城市化水平、人均GDP和能源强度对CO2排放的影响。结果显示,人口总量、城市化水平、人均GDP和能源强度与CO2排放存在长期均衡关系。长期来看,人口总量对CO2排放量的影响最大,城市化水平其次,人均GDP最小。短期来看,能源强度和人均GDP对CO2排放波动影响较大,而人口总量和城市化水平影响不显著。     

Household structure and housing needs

This paper is mainly derived from the material presented in the preceding article by S. P. Brown. Indeed, while the previous analysis is of considerable intrinsic interest, the hypothetical population was constructed and its family distribution was shown for the purpose of providing a basis for estimates of housing needs. For several reasons it appeared to be essential to have such a basis. First, any housing programme has to take the future, as well as the present, distribution of households by type and size into account. Secondly, such a programme has to be designed so as not to prevent household formation—there should be dwellings for all potential households, so that involuntary doubling-up need not occur. Thirdly, most residential areas should have dwellings for an eventually stable population, that is, for one which has variety of age groups and of household types, and also fair stability of housing demand. Estimates of the distribution of potential ‘households’ could be derived from the ‘family’ distribution of the hypothetical population which reflects current demographic trends. Thus although this population is a ‘hypothetical’ one, it provides a realistic premise for considering housing needs, and because it is a ‘stationary’ one, it provides an especially suitable premise. Moreover, since the demographic characteristics of its ‘families’ and therefore of its potential households were established in far greater detail than has ever been the case in sample surveys of existing households, it was possible to classify households in the terms which appear to be most appropriate for the first draft of a housing programme, irrespective of social and economic variations in demand.

The first stage in following up Mr Brown's analysis was the conversion of ‘families’ into ‘households’. Two examples of the possible household distribution of the hypothetical population are presented. Example A, which gives a realistic, but not extreme, picture of the conversion of families into households, is used for the subsequent detailed analysis, while broader figures for distribution B are also included.

In the second stage the various types of household had to be distinguished. For estimating housing needs, two interrelated criteria of household classification are relevant—first, the stage in the life of a household, especially appropriate in considering space requirements; secondly, the age composition of households, which largely determines the type of dwelling needed.

The detailed distribution of households by size and type, based on this classification, is further translated into a distribution of dwellings by type and size. For this purpose, additional assumptions about the number of rooms and the type of dwelling needed by households of various types are introduced and applied to the hypothetical population, both to household distributions A and B. These assumptions are not based on accepted standards, nor do they suggest standards. They are merely used for the purpose of illustrating a possible method of estimating housing needs on the basis of a detailed picture of household structure. They are further designed to represent one possible compromise between economy in dwelling distribution, on the one hand, and flexibility of space for individual households, on the other.

In the final sections of the paper, the implications of the dwelling distributions here presented are discussed in relation to household mobility, and also with reference to the necessity for reconciling short-term and long-term housing needs in any housing programme.     

Two Latent Groups Influencing Subjective Social Status: Middle Class Tendency and Clear Class Consciousness

Social Indicators Research - The extent to which socio-economic factors other than income and household size are associated with household CO2 emissions and whether associations vary across...