Jan Minx

Growth in emission transfers via international trade from 1990 to 2008

Despite the emergence of regional climate policies, growth in global CO2 emissions has remained strong. From 1990 to 2008 CO2 emissions in developed countries (defined as countries with emission-reduction commitments in the Kyoto Protocol, Annex B) have stabilized, but emissions in developing countries (non-Annex B) have doubled. Some studies suggest that the stabilization of emissions in developed countries was partially because of growing imports from developing countries. To quantify the growth in emission transfers via international trade, we developed a trade-linked global database for CO2 emissions covering 113 countries and 57 economic sectors from 1990 to 2008. We find that the emissions from the production of traded goods and services have increased from 4.3 Gt CO2 in 1990 (20% of global emissions) to 7.8 Gt CO2 in 2008 (26%). Most developed countries have increased their consumption-based emissions faster than their territorial emissions, and non–energy-intensive manufacturing had a key role in the emission transfers. The net emission transfers via international trade from developing to developed countries increased from 0.4 Gt CO2 in 1990 to 1.6 Gt CO2 in 2008, which exceeds the Kyoto Protocol emission reductions. Our results indicate that international trade is a significant factor in explaining the change in emissions in many countries, from both a production and consumption perspective. We suggest that countries monitor emission transfers via international trade, in addition to territorial emissions, to ensure progress toward stabilization of global greenhouse gas emissions.

INPUT–OUTPUT ANALYSIS AND CARBON FOOTPRINTING: AN OVERVIEW OF APPLICATIONS

This article provides an overview of how generalised multi-regional input–output models can be used for carbon footprint applications. We focus on the relevance and suitability of such evidence to inform decision making. Such an overview is currently missing. Drawing on UK results, we cover carbon footprint applications in seven areas: national emissions inventories and trade, emission drivers, economic sectors, supply chains, organisations, household consumption and lifestyles as well as sub-national emission inventories. The article highlights the multiple uses of generalised multi-regional input–output models for carbon footprinting and concludes by highlighting important avenues for future research.

A "carbonizing dragon": China's fast growing CO2 emissions revisited.

Chinas annual CO(2) 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 CO(2) 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 Chinas 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 CO(2) 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 Chinas 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.

A CARBON FOOTPRINT TIME SERIES OF THE UK – RESULTS FROM A MULTI-REGION INPUT–OUTPUT MODEL

The framework and results of an international multi-region input–output (MRIO) model for the UK are presented. A time series of balanced input–output tables for the UK was constructed for the period 1992 to 2004 by using a matrix balancing procedure that is able to handle conflicting external data and inconsistent constraints. Detailed sectoral and country-specific trade data for the UK were compiled and reconciled with the UK input–output data, and economic and environmental accounts for three world regions were integrated in a UK-specific MRIO model. This was subsequently used to calculate a time series of national carbon footprints for the UK from 1992 to 2004. Greenhouse gas emissions embedded in UK trade are distinguished by destination of imports to intermediate and final demand. Most greenhouse gases show a significant increase over time in consumer emissions and a widening gap between producer and consumer emissions. Net CO2 emissions embedded in UK imports increased from 4.3% of producer emissions in 1992 to a maximum of 20% in 2002. The total estimated UK carbon footprint in 2004 was 730 Mt for CO2 and 934 Mt CO2 equivalents for all greenhouse gases.

The Impact of Social Factors and Consumer Behavior on Carbon Dioxide Emissions in the United Kingdom: A Regression Based on Input-Output and Geodemographic Consumer Segmentation Data

In this article we apply geodemographic consumer segmentation data in an input−output framework to understand the direct and indirect carbon dioxide (CO) emissions associated with consumer behavior of different lifestyles in the United Kingdom. In a subsequent regression analysis, we utilize the lifestyle segments contained in the dataset to control for aspects of behavioral differences related to lifestyles in an analysis of the impact of various socioeconomic variables on CO emissions, such as individual aspirations and peoples attitudes toward the environment, as well as the physical context in which people act. This approach enables us to (1) test for the significance of lifestyles in determining CO emissions, (2) quantify the importance of a variety of individual socioeconomic determinants, and (3) provide a visual representation of “where” the various factors exert the greatest impact, by exploiting the spatial information contained in the lifestyle data. Our results indicate the importance of consumer behavior and lifestyles in understanding CO emissions in the United Kingdom. Across lifestyle groups, CO emissions can vary by a factor of between 2 and 3. Our regression results provide support for the idea that sociodemographic variables are important in explaining emissions. For instance, controlling for lifestyles and other determinants, we find that emissions are increasing with income and decreasing with education. Using the spatial information, we illustrate how the lifestyle mix of households in the United Kingdom affects the geographic distribution of environmental impacts.

Carbon footprints of cities and other human settlements in the UK

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.

Distributional Effects of Climate Change Taxation: The Case of the UK

Current economic instruments aimed at climate change mitigation focus mainly on CO(2) emissions, but efficient climate mitigation needs to focus on other greenhouse gases as well as CO(2). This study investigates the distributional effects of climate change taxes on households belonging to different income and lifestyle groups; and it compares the effects of a CO(2) tax with a multiple GHG tax in the UK in terms of cost efficiency and distributional effects. Results show that a multi GHG tax is more efficient than a CO(2) tax due to lower marginal abatement costs, and that both taxes are regressive, with lower income households paying a relatively larger share of their income for the taxes than higher income households. A shift from a CO(2) tax to a GHG tax will reduce and shift the tax burden between consumption categories such as from energy-intensive products to food products. Consumers have different abilities to respond to the tax and change their behavior due to their own socio-economic attributes as well as the physical environment such as the age of the housing stock, location, and the availability of infrastructure. The housing-related carbon emissions are the largest component of the CO(2) tax payments for low income groups and arguments could be made for compensation of income losses and reduction of fuel poverty through further government intervention.

2 °C and SDGs: united they stand, divided they fall?

The adoption of the Sustainable Development Goals (SDGs) and the new international climate treaty could put 2015 into the history books as a defining year for setting human development on a more sustainable pathway. The global climate policy and SDG agendas are highly interconnected: the way that the climate problem is addressed strongly affects the prospects of meeting numerous other SDGs and vice versa. Drawing on existing scenario results from a recent energy-economy-climate model inter-comparison project, this letter analyses these synergies and (risk) trade-offs of alternative 2 °C pathways across indicators relevant for energy-related SDGs and sustainable energy objectives. We find that limiting the availability of key mitigation technologies yields some co-benefits and decreases risks specific to these technologies but greatly increases many others. Fewer synergies and substantial trade-offs across SDGs are locked into the system for weak short-term climate policies that are broadly in line with current Intended Nationally Determined Contributions (INDCs), particularly when combined with constraints on technologies. Lowering energy demand growth is key to managing these trade-offs and creating synergies across multiple energy-related SD dimensions. We argue that SD considerations are central for choosing socially acceptable 2 °C pathways: the prospects of meeting other SDGs need not dwindle and can even be enhanced for some goals if appropriate climate policy choices are made. Progress on the climate policy and SDG agendas should therefore be tracked within a unified framework.

Effects of China's Economic Growth

In a recent Policy Forum, J. Liu reviewed “Chinas road to sustainability” (2 April, p. [50][1]). Liu focused on population growth and an increase in the number of households, but he failed to adequately address the most important socioeconomic driver behind environmental degradation in China:

International flows of embodied CO 2 with an application to aluminium and the EU ETS

The growth of international trade is increasing the separation between the location of consumption and the location of production and emissions. As a consequence of this growth, GHG emissions reported on a territorial basis can differ markedly from the global emissions required to produce the products consumed in a region. An overview is provided of emissions embodied in international trade, including an assessment of the role of both intermediate and final goods, and the impact that these flows have on territorial emissions. The global flows of embodied emissions driven by the EU consumption of aluminium are then examined in the context of the planned extension of the EU Emissions Trading Scheme (EU ETS), which is to include emissions from aluminium production from 2013 onwards. At the aggregate level, it is found that only around one-third of embodied CO2 emissions in aluminium consumed in Europe are subject to a cost of CO2 under the EU ETS, although this may vary for individual products or producers. The data and analysis presented on the international flows of emissions embodied in aluminium are relevant for evaluation of mitigation options for the European consumption of aluminium and other emissions-intensive sectors. Although the method is robust, the analysis can be made more policy relevant and specific with more disaggregated data sets.