Energyscapes: linking the energy system and ecosystem services in real landscapes

The drive for sustainable energy production is leading to increased deployment of land based renewables. Although there is public support, in principle, for renewable energy at a national level, major resistance to renewable energy technologies often occurs at a local level. Within this context, it can be useful to consider the "energyscape" which we initially define as the complex spatial and temporal combination of the supply, demand and infrastructure for energy within a landscape. By starting with a consideration of the energyscape, we can then consider the positive and negative interactions with other ecosystem services within a particular landscape. This requires a multidisciplinary systems-approach that uses existing knowledge of landscapes, energy options, and the different perspectives of stakeholders. The approach is examined in relation to pilot case-study comprising a 155 km2 catchment in Bedfordshire, England

Ecological Network Analysis for Carbon Metabolism of Eco-industrial Parks: A Case Study of a Typical Eco-industrial Park in Beijing

Energy production and industrial processes are crucial economic sectors accounting for about 62% of greenhouse gas (GHG) emissions globally in 2012. Eco-industrial parks are practical attempts to mitigate GHG emissions through cooperation among businesses and the local community in order to reduce waste and pollution, efficiently share resources, and help with the pursuit of sustainable development. This work developed a framework based on ecological network analysis to trace carbon metabolic processes in eco-industrial parks and applied it to a typical eco-industrial park in Beijing. Our findings show that the entire metabolic system is dominated by supply of primary goods from the external environment and final demand. The more carbon flows through a sector, the more influence it would exert upon the whole system. External environment and energy providers are the most active and dominating part of the carbon metabolic system, which should be the first target to mitigate emissions by increasing efficiencies. The carbon metabolism of the eco-industrial park can be seen as an evolutionary system with high levels of efficiency, but this may come at the expense of larger levels of resilience. This work may provide a useful modeling framework for low-carbon design and management of industrial parks

Virtual Scarce Water in China

Water footprints and virtual water flows have been promoted as important indicators to characterize human-induced water consumption. However, environmental impacts associated with water consumption are largely neglected in these analyses. Incorporating water scarcity into water consumption allows better understanding of what is causing water scarcity and which regions are suffering from it. In this study, we incorporate water scarcity and ecosystem impacts into multiregional input–output analysis to assess virtual water flows and associated impacts among 30 provinces in China. China, in particular its water-scarce regions, are facing a serious water crisis driven by rapid economic growth. Our findings show that inter-regional flows of virtual water reveal additional insights when water scarcity is taken into account. Consumption in highly developed coastal provinces is largely relying on water resources in the water-scarce northern provinces, such as Xinjiang, Hebei, and Inner Mongolia, thus significantly contributing to the water scarcity in these regions. In addition, many highly developed but water scarce regions, such as Shanghai, Beijing, and Tianjin, are already large importers of net virtual water at the expense of water resource depletion in other water scarce provinces. Thus, increasingly importing water-intensive goods from other water-scarce regions may just shift the pressure to other regions, but the overall water problems may still remain. Using the water footprint as a policy tool to alleviate water shortage may only work when water scarcity is taken into account and virtual water flows from water-poor regions are identified

Lifting China’s Water Spell

China is a country with significant but unevenly distributed water resources. The water stressed North stays in contrast to the water abundant and polluted South defining China’s current water environment. In this paper we use the latest available data sets and adopt structural decomposition analysis for the years 1992 to 2007 to investigate the driving forces behind the emerging water crisis in China. We employ four water indicators in China, that is, freshwater consumption, discharge of COD (chemical oxygen demand) in effluent water, cumulative COD and dilution water requirements for cumulative pollution, to investigate the driving forces behind the emerging crisis. The paper finds water intensity improvements can effectively offset annual freshwater consumption and COD discharge driven by per capita GDP growth, but that it had failed to eliminate cumulative pollution in water bodies. Between 1992 and 2007, 225 million tonnes of COD accumulated in Chinese water bodies, which would require 3.2–8.5 trillion m³ freshwater, depending on the water quality of the recipient water bodies to dilute pollution to a minimum reusable standard. Cumulative water pollution is a key driver to pollution induced water scarcity across China. In addition, urban household consumption, export of goods and services, and infrastructure investment are the main factors contributing to accumulated water pollution since 2000

A “Carbonizing Dragon”: China’s Fast Growing CO₂ Emissions Revisited

China’s annual CO₂ emissions grew by around 4 billion tonnes between 1992 and 2007. More than 70% of this increase occurred between 2002 and 2007. While growing export demand contributed more than 50% to the CO₂ emission growth between 2002 and 2005, capital investments have been responsible for 61% of emission growth in China between 2005 and 2007. We use structural decomposition analysis to identify the drivers for China’s emission growth between 1992 and 2007, with special focus on the period 2002 to 2007 when growth was most rapid. In contrast to previous analysis, we find that efficiency improvements have largely offset additional CO₂ emissions from increased final consumption between 2002 and 2007. The strong increases in emissions growth between 2002 and 2007 are instead explained by structural change in China’s economy, which has newly emerged as the third major emission driver. This structural change is mainly the result of capital investments, in particular, the growing prominence of construction services and their carbon intensive supply chain. By closing the model for capital investment, we can now show that the majority of emissions embodied in capital investment are utilized for domestic household and government consumption (35–49% and 19–36%, respectively) with smaller amounts for the production of exports (21–31%). Urbanization and the associated changes in lifestyle are shown to be more important than other socio-demographic drivers like the decreasing household size or growing population. We argue that mitigation efforts will depend on the future development of these key drivers, particularly capital investments which dictate future mitigation costs

China's ecological footprint via biomass import and consumption is increasing

Here we uploaded the results developed in the manuscript: "China's ecological footprint via biomass import and consumption is increasing". The results contained consumption-based HANPP of different countries from 2004 to 2017, HANPP flows between China and other countries, sectoral HANPP intensity of different income groups in 2017, and variability range in key data and assumptions for high and low HANPP estimates.</p

Extended territorial (scope 1 and 2) per capita CO₂ emissions and per capita CF by human settlement types in England: 1—major urban (<em>N</em> = 76); 2—large urban (<em>N</em> = 45); 3—other urban (<em>N</em> = 55); 4—significant rural (<em>N</em> = 53); 5—rural 50 (<em>N</em> = 52); 6—rural 80 (<em>N</em> = 73)

<p><strong>Figure 4.</strong> Extended territorial (scope 1 and 2) per capita CO₂ emissions and per capita CF by human settlement types in England: 1—major urban (<em>N</em> = 76); 2—large urban (<em>N</em> = 45); 3—other urban (<em>N</em> = 55); 4—significant rural (<em>N</em> = 53); 5—rural 50 (<em>N</em> = 52); 6—rural 80 (<em>N</em> = 73). Further definitions are provided in the supplementary information (available at <a href="http://stacks.iop.org/ERL/8/035039/mmedia" target="_blank">stacks.iop.org/ERL/8/035039/mmedia</a>). The figure shows that there is a clear positive relationship between average extended territorial per capita CO₂ emissions and increasingly rural human settlement types. This is not the case for per capita CFs.</p> <p><strong>Abstract</strong></p> <p>A growing body of literature discusses the CO₂ emissions of cities. Still, little is known about emission patterns across density gradients from remote rural places to highly urbanized areas, the drivers behind those emission patterns and the global emissions triggered by consumption in human settlements—referred to here as the carbon footprint. In this letter we use a hybrid method for estimating the carbon footprints of cities and other human settlements in the UK explicitly linking global supply chains to local consumption activities and associated lifestyles. This analysis comprises all areas in the UK, whether rural or urban. We compare our consumption-based results with extended territorial CO₂ emission estimates and analyse the driving forces that determine the carbon footprint of human settlements in the UK. Our results show that 90% of the human settlements in the UK are net importers of CO₂ emissions. Consumption-based CO₂ emissions are much more homogeneous than extended territorial emissions. Both the highest and lowest carbon footprints can be found in urban areas, but the carbon footprint is consistently higher relative to extended territorial CO₂ emissions in urban as opposed to rural settlement types. The impact of high or low density living remains limited; instead, carbon footprints can be comparatively high or low across density gradients depending on the location-specific socio-demographic, infrastructural and geographic characteristics of the area under consideration. We show that the carbon footprint of cities and other human settlements in the UK is mainly determined by socio-economic rather than geographic and infrastructural drivers at the spatial aggregation of our analysis. It increases with growing income, education and car ownership as well as decreasing household size. Income is not more important than most other socio-economic determinants of the carbon footprint. Possibly, the relationship between lifestyles and infrastructure only impacts carbon footprints significantly at higher spatial granularity.</p

Overview of methodology for estimating detailed local final demand matrices for municipalities in the UK

<p><strong>Figure 1.</strong> Overview of methodology for estimating detailed local final demand matrices for municipalities in the UK.</p> <p><strong>Abstract</strong></p> <p>A growing body of literature discusses the CO₂ emissions of cities. Still, little is known about emission patterns across density gradients from remote rural places to highly urbanized areas, the drivers behind those emission patterns and the global emissions triggered by consumption in human settlements—referred to here as the carbon footprint. In this letter we use a hybrid method for estimating the carbon footprints of cities and other human settlements in the UK explicitly linking global supply chains to local consumption activities and associated lifestyles. This analysis comprises all areas in the UK, whether rural or urban. We compare our consumption-based results with extended territorial CO₂ emission estimates and analyse the driving forces that determine the carbon footprint of human settlements in the UK. Our results show that 90% of the human settlements in the UK are net importers of CO₂ emissions. Consumption-based CO₂ emissions are much more homogeneous than extended territorial emissions. Both the highest and lowest carbon footprints can be found in urban areas, but the carbon footprint is consistently higher relative to extended territorial CO₂ emissions in urban as opposed to rural settlement types. The impact of high or low density living remains limited; instead, carbon footprints can be comparatively high or low across density gradients depending on the location-specific socio-demographic, infrastructural and geographic characteristics of the area under consideration. We show that the carbon footprint of cities and other human settlements in the UK is mainly determined by socio-economic rather than geographic and infrastructural drivers at the spatial aggregation of our analysis. It increases with growing income, education and car ownership as well as decreasing household size. Income is not more important than most other socio-economic determinants of the carbon footprint. Possibly, the relationship between lifestyles and infrastructure only impacts carbon footprints significantly at higher spatial granularity.</p

Extended territorial (scope 1 and 2) per capita CO₂ emissions and per capita CFs across 354 municipalities in England

<p><strong>Figure 3.</strong> Extended territorial (scope 1 and 2) per capita CO₂ emissions and per capita CFs across 354 municipalities in England. Rural (green) and urban (yellow) settlement types are distinguished. Robust regression lines for the two subsamples are also shown. The graph shows that per capita CFs are larger than extended territorial per capita CO₂ emissions for most municipalities represented by all data points to the right of the diagonal (dashed) line. The regression lines show that this is pattern is more evident for urban municipalities. The 95% confidence interval for urban, as opposed to rural, does not include 1 (45° line). Also, the error of regression for urban is twice as large as for the rural subsample, indicating a much more consistent behaviour for rural municipalities. Inset enlarges for the following range: <em>x</em>-axis 10–15 tCO₂/cap, <em>y</em>-axis 4–16 tCO₂/cap.</p> <p><strong>Abstract</strong></p> <p>A growing body of literature discusses the CO₂ emissions of cities. Still, little is known about emission patterns across density gradients from remote rural places to highly urbanized areas, the drivers behind those emission patterns and the global emissions triggered by consumption in human settlements—referred to here as the carbon footprint. In this letter we use a hybrid method for estimating the carbon footprints of cities and other human settlements in the UK explicitly linking global supply chains to local consumption activities and associated lifestyles. This analysis comprises all areas in the UK, whether rural or urban. We compare our consumption-based results with extended territorial CO₂ emission estimates and analyse the driving forces that determine the carbon footprint of human settlements in the UK. Our results show that 90% of the human settlements in the UK are net importers of CO₂ emissions. Consumption-based CO₂ emissions are much more homogeneous than extended territorial emissions. Both the highest and lowest carbon footprints can be found in urban areas, but the carbon footprint is consistently higher relative to extended territorial CO₂ emissions in urban as opposed to rural settlement types. The impact of high or low density living remains limited; instead, carbon footprints can be comparatively high or low across density gradients depending on the location-specific socio-demographic, infrastructural and geographic characteristics of the area under consideration. We show that the carbon footprint of cities and other human settlements in the UK is mainly determined by socio-economic rather than geographic and infrastructural drivers at the spatial aggregation of our analysis. It increases with growing income, education and car ownership as well as decreasing household size. Income is not more important than most other socio-economic determinants of the carbon footprint. Possibly, the relationship between lifestyles and infrastructure only impacts carbon footprints significantly at higher spatial granularity.</p

Per capita CF of 434 municipalities in the UK

<p><strong>Figure 2.</strong> Per capita CF of 434 municipalities in the UK. Inset shows London and municipalities that border within 50 km radius from centre. There is no clear pattern in terms of the geographic positioning of high and low CF municipalities. Only around London is a ring of municipalities with a high CF, while municipalities within Greater London tend to have a below average CF.</p> <p><strong>Abstract</strong></p> <p>A growing body of literature discusses the CO₂ emissions of cities. Still, little is known about emission patterns across density gradients from remote rural places to highly urbanized areas, the drivers behind those emission patterns and the global emissions triggered by consumption in human settlements—referred to here as the carbon footprint. In this letter we use a hybrid method for estimating the carbon footprints of cities and other human settlements in the UK explicitly linking global supply chains to local consumption activities and associated lifestyles. This analysis comprises all areas in the UK, whether rural or urban. We compare our consumption-based results with extended territorial CO₂ emission estimates and analyse the driving forces that determine the carbon footprint of human settlements in the UK. Our results show that 90% of the human settlements in the UK are net importers of CO₂ emissions. Consumption-based CO₂ emissions are much more homogeneous than extended territorial emissions. Both the highest and lowest carbon footprints can be found in urban areas, but the carbon footprint is consistently higher relative to extended territorial CO₂ emissions in urban as opposed to rural settlement types. The impact of high or low density living remains limited; instead, carbon footprints can be comparatively high or low across density gradients depending on the location-specific socio-demographic, infrastructural and geographic characteristics of the area under consideration. We show that the carbon footprint of cities and other human settlements in the UK is mainly determined by socio-economic rather than geographic and infrastructural drivers at the spatial aggregation of our analysis. It increases with growing income, education and car ownership as well as decreasing household size. Income is not more important than most other socio-economic determinants of the carbon footprint. Possibly, the relationship between lifestyles and infrastructure only impacts carbon footprints significantly at higher spatial granularity.</p