Stochastic Impacts By Regression On Population, Affluence, And Technology Research Articles

Exploring City Development Modes under the Dual Control of Water Resources and Energy-Related CO2 Emissions: The Case of Beijing, China

Water and energy are basic resources for urban development. It is of extreme importance to balance economic development, water and energy security, and environmental sustainability at the city level. Although many studies have focused on energy-related CO2 emissions or water resources, individually, in relation to socioeconomic development, few studies have considered water and energy-related CO2 emissions as synchronous limiting factors. Here, taking Beijing as an example, a partial least squares STIRPAT model—a method that combines partial least squares with the STIRPAT (stochastic impacts by regression on population, affluence, and technology) model—was used to determine the main driving factors of water use and energy-related CO2 emissions at the regional scale from 1996 to 2016. The empirical results showed that the population, per capita gross domestic product (GDP), urbanization level, technology level, and service level, are all important factors that influence the total water use and energy-related CO2 emissions. Additionally, eight scenarios were established to explore suitable development modes for future years. Consequently, a medium growth rate in socioeconomic status and population, and a high growth rate in the technology and service level, were found to be the most appropriate development modes. This scenario would result in a total water use of 4432.13 million m3 and energy-related CO2 emissions of 173.64 million tons in 2030. The results provide a new perspective for decision makers to explore suitable measures for simultaneously conserving water resources and reducing energy-related CO2 emissions in the context of urban development.

How do population and land urbanization affect CO2 emissions under gravity center change? A spatial econometric analysis

We examines the movement trajectory of gravity centers of China's CO2 emissions on a regional level based on a gravity model, and then investigate the impact of urbanization on CO2 emissions by utilizing an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model under the change of gravity center. We measure the urbanization in different dimensions including both population and land urbanization. Besides, a panel data between 2005 and 2014 is used for our empirical estimation, and the Spatial Durbin Panel model is used for estimation. The results show that the movement trajectory of gravity centers as a whole moved towards the northwest over the period. With CO2 emissions distribution presenting the characteristics of spatial agglomeration, we utilize the spatial econometric model to capture spatiality. The results show that the effect of population urbanization is insignificant; however, population urbanization has a positive and significant spatial spillover effect. Meanwhile, we find that the impact of land urbanization is significantly positive, while its spatial spillover effect is insignificant. Regarding other socioeconomic factors, it is proved that population scale, energy intensity and GDP per capita have a significantly positive impact, while industrialization level has a negative influence. These novel methodology and findings reveal that policy makers should carefully consider the characteristics of the rapid urbanization growth in China through the establishment of low-carbon urbanization policy standards, and strategies should emphasize China's land-use conditions and promote coordinate development between population urbanization and land urbanization to achieve the sustainable development of urbanization and a low-carbon economy.

Evolving Temporal–Spatial Trends, Spatial Association, and Influencing Factors of Carbon Emissions in Mainland China: Empirical Analysis Based on Provincial Panel Data from 2006 to 2015

Based on provincial panel data from 2005 to 2016, this paper analyzes evolving temporal–spatial trends, spatial correlation and influencing factors of carbon emissions in China. The results show that there is a great heterogeneity in the evolving temporal–spatial trends of carbon emissions among provinces and regions in China, with the heterogeneity in eastern provinces most obvious. At the same time, there exists significant spatial correlation and agglomeration of carbon emissions in 30 provinces. It is found that the distribution characteristics of carbon emissions are affected by various economic and social factors based on the extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Population pressure, affluence, energy intensity, industrial structure, urbanization level and investment in fixed assets can significantly promote the increase of carbon emissions. The technological level and government environmental supervision have significant inhibitory effects on carbon emissions, but foreign direct investment (FDI) has no significant impact. Therefore, it is necessary to strengthen environmental supervision and upgrade technology level to promote carbon emission reduction.

Impact of technological innovation on CO2 emissions and emissions trend prediction on ‘New Normal’ economy in China

As the largest carbon emitter in the world, China faces up with great pressure to reduce emissions. China's economy stepped into a status so called ‘New Normal’, in which major change is taking place in terms of growth rate & force, development model, and economic structure. Firstly, an extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was established to study the impact of technological innovation on carbon dioxide (CO2) emissions based on Chinese provincial panel data from 1997 to 2015. Secondly, the sample was classified into two groups, namely high-speed growth group and low-speed growth group, and the extended STIRPAT model was used to investigate the impact of technological innovation on CO2 emissions in these two groups. Thirdly, scenario simulations were herein introduced to predict CO2 emissions in 2016–2030 as economy stepped into the ‘New Normal’. Finally, logistic equation was applied to predict CO2 emissions during the period of 2016–2030. It was deduced that in comparison with the high-speed growth group, independent innovation makes a greater contribution on promoting CO2 emissions as the economy keeps a slowly growth rate. Besides, the contribution of introducing innovation on CO2 emissions was proven to be essentially marginal as for the low-speed growth group although it has significant impact effect on reducing CO2 emissions from the high-speed growth group. The prediction indicates new progress on CO2 emissions reduction in the future China will be achieved as appropriate policy on technological innovation is issued by government in the ‘New Normal’ period.

The Spatial-Temporal Characteristics and Influential Factors of NOx Emissions in China: A Spatial Econometric Analysis.

While the progress of China’s industrialization and urbanization has made great strides, atmospheric pollution has become the norm, with a wide range of influence and difficult governance. While many previous works on NOx pollution have been developed from the perspectives of natural science and technology, few studies have been conducted from social-economic points of view, and regional differences have not been given adequate attention in driving force models. This paper adopts China’s provincial panel data from 2006 to 2015, an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model, and spatial econometric models to investigate the socio-economic influential factors and spatial-temporal patterns of NOx emissions. According to the spatial correlation analysis results, the provincial NOx emission changes not only affected the provinces themselves, but also neighboring regions. Spatial econometric analysis shows that the spatial effect largely contributes to NOx emissions. The other explanatory variables all have positive impacts on NOx emissions, except for the vehicular indicator (which did not pass the significance test). As shown through the estimated consequences of direct and indirect effects, the indicators have significant positive effects on their own areas, and exacerbate NOx pollution. In terms of indirect effects, only three factors passed the significant test. An increase in gross domestic product (GDP) and energy consumption will exacerbate adjacent NOx pollution. Finally, a series of socio-economic measures and regional cooperation policies should be applied to improve the current air environment in China.

Drivers of greenhouse gas emissions in the United States: revisiting STIRPAT model

The challenge of reducing emissions of greenhouse gases (GHG) has stimulated great attention among policymakers and scholars in recent past, and a number of STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) studies on carbon emissions have been conducted. This paper contributes to that literature by: (i) studying per capita GHG emissions in the United States (US) adopting STIRPAT modeling framework; (ii) employing new explanatory factors like cattle population density, political willingness to address environmental problems, and educational attainment; and (iii) investigating whether emissions elasticities of various factors vary within the US or not. State-level panel data over the period 1990–2014 are used, and partitioning of the sample is done with respect to two controlling factors: an indicator of political support to environmentalism and educational attainment. Results of heterogeneous slope parameters panel data models indicate that cattle density and affluence are major drivers of per capita GHG emissions in the continental US. We find strong evidence of heterogeneity in emissions elasticities across partitioned samples. Our grouping analysis suggests that in a diverse country like US, policymakers should not focus on the average relationships dictated by a single STIRPAT equation, but should account for regional differences if they want accuracy and higher effectiveness in climate policymaking.

Does intensive land use promote a reduction in carbon emissions? Evidence from the Chinese industrial sector

Human land use activity is an important contributor to the growth of CO2 emissions. Thus, reasonable land use is an effective method of reducing CO2 emissions. Based on an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the impacts of population size, wealth level, technology level, and four representative indicators of intensive industrial land use (IILU) on CO2 emissions in China from 2006 to 2015 were estimated. The results showed that research and development (R&D) investment intensity is negatively related to CO2 emissions in all three regions (east, central and west) of China; labor intensity is negatively related to CO2 emissions in the eastern region; and capital intensity is negatively related to CO2 emissions in the western region. We further calculated the contribution of changes in each driving factor to the growth of CO2 emissions in the study period, and the results showed that changes in R&D investment intensity contributed the most to CO2 emissions reductions, whereas the decrease in the labor intensity in the central region and the increase in the capital intensity in the western region show weaker but similar effects. Targeted policy implications are proposed to adjust the mode of IILU from the perspectives of labor, energy, capital and R&D investments in the three regions across China.

What causes PM2.5 pollution? Cross-economy empirical analysis from socioeconomic perspective

Is it true that, as the mainstream intuition asserts, urbanization and industrialization are the two main socioeconomic drivers of PM2.5? How do the two trends affect PM2.5 emission? This paper quantitatively analyzes the socioeconomic drivers of PM2.5 through assessment on Stochastic Impacts by Regression on Population, Affluence and Technology (STRIPAT), based on the panel data of 79 developing countries over 2001–2010. The average levels of PM2.5 pollution are calculated using remote sensing data, overcoming the difficulties that developing countries are in lack of PM2.5 monitors and that point data cannot reflect the overall level of PM2.5 pollution on a large scale. Squared terms of income and urbanization and their cross term are included in the regression models respectively to analyze the possible heterogeneous impacts on PM2.5 emissions in different development stages. The results show that income, urbanization and service sector have significant impact on PM2.5 pollution. Income has a positive effect on PM2.5 all the time but the effect decreases as the level of urbanization or income goes up. An inverted U relationship exists between urbanization and PM2.5, in which PM2.5 pollution positively correlates with a low level of income or urbanization but negatively at a high level. Policy recommendations from the perspective of macro-level social and economic regulation are provided for developing economies to reduce PM2.5 pollution.

The Scale, Structure and Influencing Factors of Total Carbon Emissions from Households in 30 Provinces of China—Based on the Extended STIRPAT Model

Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the total carbon emissions of households in 30 provinces of China from 2006 to 2015, and based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the factors influencing the total carbon emissions of households were analyzed. The results indicated that, first, over the past ten years, the energy and products carbon emissions from China’s households have demonstrated a rapid growth trend and that regional distributions present obvious differences. Second, China’s energy carbon emissions due to household consumption primarily derived from the residents’ consumption of electricity and coal; China’s products household carbon emissions primarily derived from residents’ consumption of the high carbon emission categories: residences, food, transportation and communications. Third, in terms of influencing factors, the number of households in China plays a significant role in the total carbon emissions of China’s households. The ratio of children 0–14 years old and gender ratio (female = 100) are two factors that reflect the demographic structure, have significant effects on the total carbon emissions of China’s households, and are all positive. Gross Domestic Product (GDP) per capita plays a role in boosting the total carbon emissions of China’s households. The effect of the carbon emission intensity on total household carbon emissions is positive. The industrial structure (the proportion of secondary industries’ added value to the regional GDP) has curbed the growth of total carbon emissions from China’s household consumption. The results of this study provide data to support the assessment of the total carbon emissions of China’s households and provide a reasonable reference that the government can use to formulate energy-saving and emission-reduction measures.

A Spatial Panel Data Analysis of Economic Growth, Urbanization, and NOx Emissions in China.

Is nitrogen oxides emissions spatially correlated in a Chinese context? What is the relationship between nitrogen oxides emission levels and fast-growing economy/urbanization? More importantly, what environmental preservation and economic developing policies should China’s central and local governments take to mitigate the overall nitrogen oxides emissions and prevent severe air pollution at the provincial level in specific locations and their neighboring areas? The present study aims to tackle these issues. This is the first research that simultaneously studies the nexus between nitrogen oxides emissions and economic development/urbanization, with the application of a spatial panel data technique. Our empirical findings suggest that spatial dependence of nitrogen oxides emissions distribution exists at the provincial level. Through the investigation of the existence of an environmental Kuznets curve (EKC) embedded within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework, we conclude something interesting: an inverse N-shaped EKC describes both the income-nitrogen oxides nexus and the urbanization-nitrogen oxides nexus. Some well-directed policy advice is provided to reduce nitrogen oxides in the future. Moreover, these results contribute to the literature on development and pollution.

Analyzing and optimizing the impact of economic restructuring on Shanghai’s carbon emissions using STIRPAT and NSGA-II

The economic restructuring of cities has a significant impact on their carbon emissions and is an important pathway to low-carbon development. China is the world’s largest carbon emitter, but few studies provide an in-depth analysis of how economic restructuring is affecting carbon emissions at its city level. This study develops a Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model to analyze the impact of economic restructuring on CO2 emissions in Shanghai. The results suggest that Shanghai’s emissions have remained stable post-2007, largely due to the city’s economic restructuring in favor of the tertiary sector: every 1% increase in the tertiary sector’s share of GDP is associated with a 0.76% reduction in CO2 emissions. This study also uses a multi-objective genetic algorithm, specifically the Non-dominated Sorting Genetic Algorithm II (NSGA-II), to optimize economic restructuring of Shanghai with regard to economic and climate objectives. The result suggests that Shanghai should aim to reduce the industrial share of gross output from 49.4% in 2012 to 38.3% in 2020. The main conclusion of the study is that Shanghai and, by extension, other Chinese cities, cannot achieve their climate targets without making meaningful changes to the economy geared towards less carbon-intensive activities.

Ex-post assessment of the Kyoto Protocol – quantification of CO2 mitigation impact in both Annex B and non-Annex B countries-

The Kyoto Protocol implemented the first international top-down mechanisms and provided mitigation incentives for both Annex B and non-Annex B countries. An assessment of the Kyoto Protocol would contribute to the fundamental discussion on designing future mitigation mechanisms under Article 6 of the Paris Agreement, while the use of a top-down approach that includes an emission trading scheme appears important for achieving the 2 °C target. This paper summarizes the existing literature and quantifies the effects of greenhouse gas (GHG) emission reduction impacts from the Kyoto Protocol using the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) approach for Annex B countries and 'additionality' assessment of Clean Development Mechanisms (CDM) projects for non-Annex B countries. We found that the Kyoto Protocol brought about 951 Mt CO2e of real emission reductions in all over the world, mainly from implementing non-energy-related GHG emission reduction projects in non-Annex B countries. For the Annex B countries, 76 Mt CO2e of mitigation occurred during the preparation stage of the first commitment period (2005–2007), but no further effects were observed during the first commitment period (2008–2012). The following important lessons were learned from the implementation of the Kyoto Protocol: (1) insufficient emission caps did not provide any mitigation incentives; rather, they resulted in perverse effects amounting to 12 Mt CO2e, which increased emissions in certain Annex B countries with economies in transition; and (2) since 42% of energy-related projects in non-Annex B countries were assessed as non-additional projects, more attention needs to be paid to the design of international cooperation mechanisms.

Environmental impact assessment of ecological migration in China: a survey of immigrant resettlement regions

Implementation of ecological migration (eco-migration) policies may improve the fragile ecological environment of emigration areas; however, it also places enormous pressure on the human-environment systems in immigrant resettlement regions. Via the application of participatory rural appraisal (PRA) methods, ecological footprint (EF), and stochastic impacts by regression on population, affluence, and technology (STIRPAT) models, 21 villages of Huanjiang County in Guangxi Zhuang Autonomous Region, China were used in this research as a case study area for the environmental appraisal of eco-migration policies in immigrant resettlement regions. Results show: (1) In the past 20 years of implementing eco-migration policies, the EF per capita constantly increased, the biocapacity (BC) per capita constantly decreased, and the ecological deficit gradually increased, indicating an extremely negative impact of eco-migration projects on the ecological environment in the immigration areas. (2) Cropland and forest land are the most important components of the per-capita EF. The per-capita EF of cropland experienced overall a first increasing and then decreasing trend, and the per-capita EF of forest land constantly increased and showed the most rapid increase among all types of EF in the last two decades. (3) The proportion of per-capita EF of different types of productive land is in the order of forest land > cropland > carbon uptake land > built-up land > grazing land from high to low, and this is a significant change from the original order of cropland > forest land > carbon uptake land > grazing land > built-up land. (4) Because of unequal possession of ecologically productive resources, the overall per-capita EF, overall per-capita BC, overall per-capita ecological deficit of productive land use by migrants, and their component values of different types of productive land use are all lower than the corresponding values of the natives. The ecological deficit of natives is more severe than that of migrants. (5) Whereas population growth and overexploitation of resources lead directly to the increased pressure on the ecological environment in the immigration areas, increasing nonagricultural income and improving the consumption structure can reduce the dependence of farmers on the land, thus inhibiting the increase of EF.

Can Energy Structure Optimization, Industrial Structure Changes, Technological Improvements, and Central and Local Governance Effectively Reduce Atmospheric Pollution in the Beijing–Tianjin–Hebei Area in China?

Economic growth in the Beijing–Tianjin–Hebei region has been achieved by consuming large amounts of fossil fuels. This produces a large number of pollutants, which damage the physical and mental health of residents, and prevent sustainable economic development. The most urgent task at present is improving the quality of the environment. This paper takes carbon emission as a pollution index, and adopts an extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model in order to study the impact of the optimization of industry structure (in particular the reduction of the proportion of energy-intensive secondary industry), the optimization of the energy structure, and technological improvements on the atmospheric environmental quality. We obtain some important and enlightening discoveries. First of all, the rapid economic growth that has been based on magnanimous fossil fuel consumption is still the main reason for the deterioration of the atmospheric environment. This means that the main driving force of economic growth still comes from high pollution industries, despite a strategy for the transformation of the pattern of economic growth having been proposed for many years. Second, the optimization of the industrial structure has not played a significant role in promoting the reduction of carbon emissions. Through further research, we believe that this may be due to the low-quality development of the third industry. In other words, the traditional service industry related to high energy consumption accounts for a large proportion in regional total output, while the high-end service industry related to small pollution accounts for a relatively small proportion. Third, reducing the consumption of coal and improving the technological level can effectively curb the deterioration of the environmental quality. In addition, we find that transboundary pollution is an important factor affecting the environment in this region, and the earnings of any unilateral treatment action is small. As a result, joint pollution control under the supervision of the central government can produce greater benefits. Therefore, we believe that the transition of the economic growth pattern, and the optimization of the energy and industry structures (especially developing the high-end service industry) are effective ways to improve the environmental quality.

PM2.5 mitigation in China: Socioeconomic determinants of concentrations and differential control policies

Elucidating the key impact factors on PM2.5 concentrations is crucial to formulate effective mitigation policies. In this study, we employed an extended Stochastic Impacts by Regression on Population Affluence and Technology (STIRPAT) model to identify the socioeconomic determinants of PM2.5 concentrations for 12 different regions and across China. The evaluation was based on a balanced panel dataset integrating long-term satellite-derived PM2.5 concentrations and socio-economic data in China from 1999 to 2011. Empirical results indicate that the influencing factors can be ranked in descending order of importance as: proportion of secondary sector of the economy, GDP per capita, urbanization, population, energy intensity, and proportion of tertiary sector. Proportion of secondary sector is the greatest contribution to increasing PM2.5 concentrations, especially for heavily polluted regions. GDP per capita is secondary in importance, and its impact is weakened by the existence of an EKC relationship between GDP per capita and PM2.5 concentrations. Therefore, PM2.5 pollution is an economic development mode problem, rather than a general economic development problem. The impact of urbanization varies across regions; while promoting urbanization will be conducive to decreased PM2.5 concentrations in Northwest China and Northeast China, it will contribute to increased PM2.5 concentrations in other regions. Population and energy intensity are significant in most regions, but neither are decisive factors because of the small absolute value of their coefficients. Finally, different combinations of mitigation policies are proposed for different regions in this study to meet the mitigation targets.

Driving forces of CO2 emissions and mitigation strategies of China’s National low carbon pilot industrial parks

In an effort to address climate change, in 2013 China launched the world’s largest government-driven carbon emission reduction programme, the National Low Carbon Industrial Parks Pilot Programme (LCIPPP). This paper analyses this newly developed pilot program. To deepen our understanding of the causes and the impact of industrial park CO2 emissions, we use the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and data from 20 pilot industrial parks involved in the LCIPPP for the period 2012–2016. This study quantitatively evaluates the effect of CO2 emissions on output, energy structure, energy intensity, industrial structure, R&D intensity, and population change in different regions and nationally through an elasticity coefficient method. The results confirm that an increase in output and energy intensity is a dominant contributor to the growth of CO2 emissions whereas an increase of the share of tertiary industry and R&D intensity has significant effects on reducing CO2 emissions. The elasticity of energy intensity and renewable energy consumption on CO2 emissions in the eastern region of China is the highest, indicating that using renewable energy to reduce CO2 emissions for the industrial parks is more effective in the eastern region as compared to the central and western regions of the country. The elasticity of population is significantly negative in both the central and western areas while it is positive in eastern part of China, thereby illustrating that promoting labour intensive industries will be an effective way to reduce CO2 emissions for the industrial parks in China’s central and western regions. Our study reveals that differentiated low carbon development pathways should be adopted. Concrete policy implications for reducing CO2 emissions are also provided.

Nonlinear modelling for greenhouse effect related to global warming incorporated with the nuclear industry using neural networking theory

The greenhouse effect related global warming is studied by the artificial intelligence (AI) based neural networking modelling. The stochastic impacts by regression on population, affluence and technology (STIRPAT) model is applied to the carbon dioxide gas based greenhouse effect. It is shown of the energy factor where four sources as oil, coal, nuclear, and renewable are connected in which the values are cumulative for comparisons with nuclear energy. The values are higher in the case of 'without nuclear'. The highest values are 1,808.78 and 6,609.68 respectively which are dimensionless and means for the global warming effectiveness. It is also described as the global warming factor for comparisons with nuclear power in which the values show higher in the later time. The study shows the dynamical transients of the negative effects in the case of the nuclear energy portion missing. Therefore, the effect of the nuclear part should be considered as one of major energy sources of a nation.

Identifying the key impact factors of carbon emission in China: Results from a largely expanded pool of potential impact factors

Carbon emission reduction (CER) comes to be the principle in most countries particularly China, the largest carbon emitter. For finding an efficient solution, the priority is to find the key impact factors (KIFs) of carbon emission. Previous studies for identifying KIFs, which partially selected only a few potential impact factors (PIFs), are inconsistent in their findings. This study aims to explore the KIFs of carbon emission in China among 43 PIFs, which comprehensively covers 30 relevant studies. The KIFs in China are identified using the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model with correlation analysis, partial correlation analysis and stepwise regression. The findings of this study are as follows: (1) China's carbon emission has five KIFs: the real GDP per capita, urbanization rate, ratio of tertiary to secondary industry, ratio of renewable energy, and fixed assets investment; (2) the most significant carbon emission contributor is real GDP per capita and the most significant carbon emission inhibitor is urbanization rate. This study provides the reliable KIFs for governors' targeted decision-making on CER, and policy implications from the identified KIFs are highlighted.

Correction: Assessment of municipal infrastructure development and its critical influencing factors in urban China: A FA and STIRPAT approach.

[This corrects the article DOI: 10.1371/journal.pone.0181917.].

Drivers of Human Stress on the Environment in the Twenty-First Century

Human actions are transforming ecosystems across the globe. Six frameworks aid in understanding the forces that drive human stress on the environment and human responses to this stress. Two of them, the stochastic impacts by regression on population, affluence and technology (STIRPAT) model and decomposition analysis, are approaches to analyzing data. Four describe the interrelated system of human actions and environmental responses: driving forces, pressures, states, impacts, responses (DPSIR); the Millennium Ecosystem Assessment (MA) framework; coupled human and natural systems (CHANS) and telecoupling; and social-ecological systems (SES). In applying these frameworks, attention must be given to the scale of analysis and to the effects of context. In addition to the frameworks there are four substantial research literatures providing theory and empirical analysis of how drivers place stress on the environment: a macrocomparative tradition, work on household energy consumption, land change science, and research on commons. Although these traditions remain somewhat separate, they are largely complementary.