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Trends in Global Greenhouse Gas Emissions from 1990-2010 Arunima Malik, Jun Lan, and Manfred Lenzen Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b06162 • Publication Date (Web): 11 Apr 2016 Downloaded from http://pubs.acs.org on April 11, 2016

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Environmental Science & Technology

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Trends in Global Greenhouse Gas Emissions from 1990-2010

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Arunima Malik1, Jun Lan1, Manfred Lenzen1*

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1ISA,

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* Corresponding author:

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e-mail: [email protected], Tel: +61 2 9351 5985; Fax: +61 2 9351 7726

School of Physics A28, The University of Sydney NSW 2006, Australia

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ACS Paragon Plus Environment


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Abstract

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Anthropogenic carbon dioxide emissions are known to alter hydrological cycles, disrupt

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marine ecosystems and species lifecycles, and cause global habitat loss. In this study we

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use a comprehensive global input-output database to assess the driving forces

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underlying the change in global CO2 emissions from 1990-2010. We decompose the

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change in emissions for the 20 year period into six mutually exclusive causal

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determinants. Our assessment of trends in fuel-use reveals that a 10.8 Peta-gram (Pg)

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rise in emissions from 1990-2010 constitutes emissions from the consumption of coal

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(49%), petroleum (25%), natural gas (17%) and biomass (9%). We demonstrate that

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affluence (per-capita consumption) and population growth are outpacing any

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improvements in carbon efficiency in driving up emissions worldwide. Our results

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strongly suggest that supply chain measures to improve technological efficiency are not

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sufficient to reduce emissions. To achieve significant emission savings, policy makers

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need to address the issue of affluence. We argue that policies to address unsustainable

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lifestyles and consumer behaviour are largely unheard of, and governments may need

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to actively intervene in non-sustainable lifestyles to achieve emission reductions. The

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results presented in this paper are vital for informing future policy decisions for

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mitigating climate change.

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46 47 48 49


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1. Introduction

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Anthropogenic greenhouse gas emissions are known to disrupt the radiative balance of

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the atmosphere, resulting in a change in climatic patterns. Research suggests that these

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emissions adversely impact the planet’s physical, ecological and biological systems1. In

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particular, carbon dioxide (CO2) emissions not only alter the Earth’s geological cycles2,

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but also disrupt marine ecosystem habitats3. In addition, there is mounting evidence

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that climate change caused by a rise in CO2 emissions is a significant contributor to an

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increase in the frequency of floods4.

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In a bid to curtail the extreme weather events and avoid the dangers of climate change,

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there is increased debate about devising mitigation strategies5. Since climate change

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caused by a rise in carbon dioxide is irreversible for 1000 years6, it is paramount that

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efforts are directed towards decreasing the concentration of these emissions in the

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atmosphere. Furthermore, previous research has identified that humans have already

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crossed the planetary boundary for climate change7. In addition to CO2 emissions,

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recent studies on land8, scarce water9, biodiversity10 and reactive nitrogen11 suggest

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that a comprehensive and integrated approach is needed to reduce the impacts of these

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pressures, and to move humanity’s metabolism in zones that the Earth system can

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process.

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anthropogenic CO2 emissions are well known and documented; research is now being

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directed towards understanding the drivers of these emissions. In order to mitigate the

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effects of CO2 emissions, analysis of key drivers of these emissions is vital for effective

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policy-making and implementation. Such an analysis can be undertaken using a

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technique called structural decomposition analysis.

In this study, we focus on CO2 emissions. Although the impacts of

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Structural decomposition analysis (SDA) is a well-developed technique for studying the

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change in physical and/or economic variables over time. It is useful for assessing the

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driving forces that underlie the change in CO2 emissions. The changes can be

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decomposed into six key determinants - carbon efficiency (e.g. changes in emissions per

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unit of output), production recipe (e.g. inputs of industries), final demand composition

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(e.g. consumption baskets), final demand destination (e.g. consumption-vs-investment

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balance), affluence (e.g. per-capita consumption) and population. SDA has advantages

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over other decomposition techniques such as Index Decomposition Analysis (IDA),

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primarily because it offers a detailed and more disaggregated view of changes occurring

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over time. In particular, SDA relies on input-output tables12. These tables document the

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flow of money between different economic sectors. In essence, input-output tables

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capture spillage effects that come about when an increase in demand in one sector

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stimulates production in sectors further up the supply chain13. This can be elegantly

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captured by mathematically inverting the input-output matrix14. If a matrix depicting

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global trade flows is used for undertaking an SDA, then the spillage effects across

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nations can be quantified and accounted for. Such global input-output matrices are so-

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called multi-regional input-output (MRIO) tables. Early practitioners of structural

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decomposition analysis used national input-output tables for quantifying the drivers of

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change. Examples of early studies include an SDA of nitrogen emissions15, air

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pollution16, water use17 or CO2 emissions18.

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With the realisation of global input-output databases in the past decade, researchers

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have now started undertaking MRIO-based structural decomposition analyses, for

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example Baiocchi and Minx19 used an MRIO model for decomposing the change in CO2

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emissions resulting from consumption in the UK between 1992 and 2004. Owen et al.20


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compared various MRIO databases to account for the variation in consumption-based

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CO2 emissions calculated across different databases. The most detailed SDA to date was

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undertaken by Arto and Dietzenbacher (A&D)21, who analysed the drivers of change in

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global CO2 emissions.

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Here, we present the results from an SDA of global CO2 emissions using the Eora MRIO

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database. Our study extends prior work mentioned above, and is novel in the following

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four ways: a) The Eora database used for this study offers more regional and sectoral

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detail with 186 world countries (representing 99.6% of global GDP)22, as opposed to the

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WIOD database used by A&D21, which only represents 40 countries, representing 85%

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of global GDP.; b) The Eora database offers a longer time-series spanning from 1990-

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2010, as opposed to the WIOD MRIO database offering the time period 1995–200823.

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Using the Eora database, we are therefore able to capture developments over a longer

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time period. ; c) A&D21 did not distinguish between different fuel types, instead they

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lumped together effects from all fuels into one variable. Here, we split the carbon

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efficiency component into separate fuel types – natural gas, coal, petroleum and

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biomass, and quantify the total change in emissions from 1990-2010 for each of these

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fuels separately. This allows us to unveil the effect of fuel substitution on changes in CO2

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emissions over a 20-year period for all world countries. ; and d) We decompose the

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change in global CO2 emissions from fuel combustion over a 20-year period, into six

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mutually exclusive determinants: carbon efficiency, final demand composition, final

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demand destination, affluence and population, whereas, A&D combined the effects of

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final demand composition and final demand destination into one determinant; however

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we present the results for these two determinants separately.



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In the following Section we present the methods underlying MRIO-based structural

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decomposition analysis. In Section 3 we present the results accompanied by a detailed

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discussion. We conclude in Section 4.

128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150


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2. Methods

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2.1 Construction of constant-price MRIO table

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In order to analyse the change in economic variables across different time periods, it is

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imperative for the multi-regional input-output (MRIO) tables to be expressed in

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constant prices. We convert the time-series Eora MRIO tables from current to constant

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prices using the “convert-first then deflate” and double deflation methods. In particular,

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we convert the IO table for each country from current to constant US$ using the

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Purchasing Power Parity (PPP) exchange rates24 published by the Organization for

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Economic Co-operation and Development (OECD). We apply Producer Price Indices

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(PPIs) published by the U.S. Bureau of Labor Statistics25 as deflators. For countries

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where PPPs are unavailable, we use market exchange rates published by the

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International Monetary Fund26. The procedure used for obtaining constant-price MRIO

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tables is explained in detail elsewhere (see Appendix in27). A&D21 also use the double

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deflation method to convert current price MRIO data from the World Input-Output

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Database (WIOD) to constant prices 23, 28.

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2.2 Structural decomposition analysis

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The central idea of structural decomposition analysis (SDA) is that changes in CO2

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emissions within a certain period can be decomposed into causal determinants, such as

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emissions intensity, production structure, final demand structure, and economic

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growth13,

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driving forces, which in turn can slow down or accelerate CO2 emissions. In this study,

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we decompose the change in CO2 emissions from 1990-2010 into six explicit

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determinants: CO2 emissions intensity (q), production recipe (L), final demand

29.

The results from an SDA can highlight trends in terms of shifts in the



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structure (u), final demand destination (v), GDP/capita (y) and population (P). For a

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detailed explanation of these variables see 29-30.

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Structural decomposition analysis requires a set of input-output tables. Formulated by

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the Nobel Prize Laureate Wassily Leontief, input-output analysis relies on a set of linear

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equations.

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= , where x is the gross output of an economy, A is the matrix of input coefficients,

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Y is the matrix of final consumption and = ( − ) is the so-called Leontief inverse

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that captures both the direct and indirect links between various industry sectors in an

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economy13. This input-output system can be generalised by incorporating data on

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environmental indicators, such as greenhouse gas emissions31 as = , where Q is

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the carbon satellite account of the entire world, and q is the direct carbon intensity of

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each industry sector, represented as Gg/$ of industry output. The generalised equation

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can be decomposed as:

The

fundamental

input-output

equation

can

be

written

∗ = ,

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as

(1)

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where q: carbon intensity of every sector, L: inter-industry structure, u: final demand

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structure, v: final demand destination, y: affluence and P: population. To illustrate,

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suppose the total carbon footprint at time 0 and 1 are and respectively, the

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change in carbon footprint ∆ ∗ can then be decomposed into following six effects

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∆ ∗ = ∆ + ∆ + ∆ + ∆ + ∆ + ∆ , !" #

%&'(. &! *!

+!,. ',.

+!,. (!-.".

/01!" !

(2)

%'*102.'"

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where ∆ represents the change in emissions due to technological improvements, ∆

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depicts the change in emissions due to the re-organization of supply-chains and

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industrial production recipes, ∆ captures the change in emissions due to consumer

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choices leading to a change in their consumption baskets, ∆ : change in emissions due ACS Paragon Plus Environment

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to the final demand destination effect such as shifts in the consumption vs. investment

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balance, ∆: change in emissions due to per-capita consumption expenditure, and ∆:

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change in emissions due to population growth.

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There are a number of approaches for undertaking structural decomposition analysis.

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Su and Ang32 summarise three most prevalent approaches: (a) the Dietzenbacher and

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Los (D&L) method33; (b) the Logarithmic Mean Divisia Index (LMDI) method34; and (c)

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the Shapley-Sun-Albrecht (SSA) method35. We adopt the D&L method in our study, since

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it is exact, non-parametrical 36, as well as zero-robust 37.

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2.3 Data sources

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The Eora MRIO database 22, 38 harbours data for over 186 countries from 1990-2010. It

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is the most detailed MRIO database to date, in comparison to other MRIO databases

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such as GTAP 39, WIOD 23, EXIOBASE 40, IDE-JETRO41 and GRAM 42. It provides the MRIO

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tables in a common sector classification and in common monetary units (US$). The data

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are available online at www.worldmrio.com43. The Eora database offers data for a range

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of environmental and social indicators, which have been used for undertaking footprint

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assessments9-10, 44.

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For obtaining the CO2 emissions data for this study, we convert the energy consumption

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data provided by International Energy Agency

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dioxide emission factors

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coal, petroleum, nuclear electricity, hydroelectric electricity, geothermal electricity,

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wind electricity, solar, tide and wave electricity, biomass and waste electricity.

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Electricity generated from fossil fuels such as coal, petroleum or natural gas emit CO2

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emissions, whilst other sources of electricity such as hydropower, wind, solar, and

46.

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using carbon content and carbon

Energy consumption data have 9 categories: natural gas,



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nuclear power are considered to be carbon-free. In order to express the CO2 emissions

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data in terms of the Eora MRIO industry sector classification, we construct concordance

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matrices to create a bridge between the CO2 emissions data and the 25 common sectoral

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classification of Eora.

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3. Results and Discussion

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3.1 Global Carbon Footprint

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3.1.1 Eora MRIO-SDA vs. WIOD MRIO-SDA of CO2 emissions

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Using the WIOD MRIO database, A&D21 find that a 8.9 Pg increase in emissions for the

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time period 1995-2008 is due to affluence (+14.0 Pg), carbon efficiency (-8.4 Pg),

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production recipe (+ 0.4 Pg), final demand (-1.5 Pg) and population (+4.2 Pg). Using the

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Eora MRIO database, we find that a 10.8 Pg increase in emissions for the time period

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1990-2010 is due to affluence (+39.3 Pg), carbon efficiency (-45.1 Pg), production

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recipe (+0.4 Pg), final demand composition (+5.5 Pg), final demand destination (+1.2

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Pg) and population (+9.5 Pg). These differences in results could be due to different base

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and end years used for the analysis. We ran our SDA for A&D's 1995-2008 time period

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using the Eora database, but our results were still not entirely the same as the ones

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presented by A&D using the WIOD database. Therefore there are other underlying

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reasons for these differences, such as different data sources used for compiling the

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databases20, and also potential sectoral aggregation47. These are explained in detail

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below.

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Owen et al. (2014)20 and Steen-Olsen et al. (2014)47 investigated the variation in the

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results for CO2 multipliers and consumption-based CO2 impact assessments when

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different MRIO databases are used. They concluded that the variations exist because of

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different source data and construction methods used for creating the MRIO tables. They

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provided the following reasons for the large differences between the Eora and the WIOD

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outcomes:

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a) Differences in emissions data: Eora and WIOD use emissions data from different

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sources. Eora’s total emissions are higher than those reported under the WIOD

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database. Eora’s emissions data come from EDGAR and IEA databases, whereas WIOD’s

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emission data are sourced from NAMEA 20.

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b) Differences in final demand and Leontief’s inverse: Owen et al. (2014)20 carried out

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structural decomposition analysis to determine the reasons for the variation between

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Eora and WIOD. They analysed the results in detail for UK, and found that the value for

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UK’s consumption-based carbon footprint is larger than that calculated using the WIOD

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database. Their analysis revealed that the differences in results can be attributed to

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different Leontief’s inverse (L) matrices. They concluded that “although Eora and WIOD

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source the domestic tables from national accounts and the trade data from UN Comtrade,

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Eora keeps the data in its original format whereas WIOD uses concordance matrices to

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transform the data to a common sector classification. This [is] one of the reasons for the

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variation”20. Furthermore, there are inherent differences in the methods used to

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populate the MRIO tables, thus leading to differences in L.

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c) Sectoral aggregation: There could also be minor differences due to the level of

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sectoral detail presented in the Eora and WIOD MRIO tables. However, we expect minor

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influence from this, since the sector number of the Eora version used for this SDA is not

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much different from the one used by A&D.

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3.1.2 Global Carbon Footprint – a world perspective

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Overall, we explain the 10.8 Peta-gram (Pg) increase between 1990 and 2010 by a 55.9

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Pg increase due to the combined effect of changes in affluence, population, final demand


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composition, production recipe and final demand destination, which is only partially

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offset by a 45.1 Pg decrease due to changes in carbon efficiency. These results are

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presented visually using a world map (Figure 1), which is the first-ever to show an SDA

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of change in emissions for virtually all individual countries.

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The carbon efficiency of most economies has improved over time (Figure 1, top left

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panel). This finding is consistent with the results presented by A&D21. However, their

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analysis is limited for only 40 countries, whilst we present the results for 186 world

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countries. Our results show that improvements in energy efficiency are primarily

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because of certain sectors becoming more energy-efficient, for example, metal

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manufacturing sectors in many countries have implemented energy-saving strategies

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that have resulted in increased carbon efficiency48. In general, technological

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improvements in vehicles and industrial processes is causing a decrease in emissions,

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aided by a variety of policy measures49. Furthermore, the Chinese government has

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invested in measures aimed at eliminating carbon-intensive industries by phasing out

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inefficient enterprises and reducing out-dated capacity50. Overall, our analysis of the

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trends in global carbon emissions indicates that carbon efficiency is the only driver that

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has shown a consistent retarding effect. All other drivers have worked as accelerators of

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emissions.

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A&D21 aggregate the results of the SDA of 40 countries into 13 broad groups. For

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example, they aggregate the results of all African countries into the Rest-of-world

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(RoW) region. This aggregation of results lacks a detailed appraisal of the change in

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emissions for individual African countries. However, we present this detail for every

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individual country in the African continent. Whilst Arto and Dietzenbacher21 conclude

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that affluence is causing an increase in emissions for all world countries, we are able to



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identify specific African countries where this is not the case. Our results show that

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affluence has indeed predominantly driven the staggering rise in carbon dioxide

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emissions for almost all countries, except in war-stricken Somalia, Tanzania and

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Democratic Republic of Congo (Figure 1).

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A&D’s21 results show that efficiency (-8.4 Pg) is out-run by affluence (+14.0 Pg) alone,

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whereas our results indicate that affluence (+39.3 Pg) and population (+4.2 Pg) trends

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together have cancelled out any emissions reductions achieved by improved carbon

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efficiency (-45.1 Pg). Selected results demonstrate that improvements in technology are

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more than out-run by the combined effect of affluence and population in many countries

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(Figure 2). Interestingly, this is particularly true for China where affluence has resulted

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in an almost eight-fold increase in emissions between 1990 and 2010.

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Our results point to the importance of addressing lifestyle and consumer demand in

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policy-making51. A direct link between household consumption and environmental

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impacts has been shown for countries such as Germany, France, the Netherlands52,

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Australia, Brazil, India, Japan, Denmark53, and China54. For example 80% of energy use

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and carbon dioxide emissions in the US are due to consumer demand of goods and

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services55. There is at present a near-absence of policy measures as governments are

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shying away from tackling unsustainable lifestyles56.

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In addition to affluence, population growth has resulted in a 9.5 Pg increase in

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emissions from 1990-2010. The population driver is particularly strong throughout

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Africa, but also for example in Pakistan and Bolivia. Global population growth is already

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a well-discussed issue for food and resources security57, and here we show that

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measures suggested for population control bear as well on climate change mitigation.

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A&D21 demonstrate that population growth is the highest in the Rest-of-World


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(aggregated African countries) region, which has driven the rise in emissions. Our world

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map captures extensive detail on specific African countries that contribute to this rise,

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for example Tanzania’s population has increased by 30% from 2002 to 201258.

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A&D21 aggregate final demand composition and final demand destination into one

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determinant, namely product mix of the consumption bundle and show that this

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determinant has caused a 1.5 Pg decrease in emissions from 1995-2008. Here, we

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present detailed results for the final demand determinants separately and demonstrate

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that these determinants have caused an increase in emissions from 1990-2010. Changes

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in the consumption baskets (final demand composition) have caused a 5.5 Pg increase

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in emissions over time, once again with particular significance throughout Africa, where

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economic development entails the displacement of traditional fuels and foods with

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more energy-intensive manufactured substitutes59. These results are different from

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those presented by A&D21., since they aggregate all African countries into one category

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and are thus unable to highlight changes in specific nations. Changes in the

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consumption-vs-investment balance (final demand destination, +1.2 Pg) have only had a

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minor effect on carbon dioxide emissions, as global investments are made at a rather

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constant level and on an ongoing basis, to compensate for depreciation and facilitate

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capacity expansion.

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Interestingly, both our and A&D’s21 results indicate that changes in industrial

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production recipes have resulted in an overall 0.4 Pg increase in emissions. This effect

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results from the cancelling out of significant accelerating and retarding contributions

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that come about when industries substitute their inputs into production with more or

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less carbon-intensive ones. Examining the detailed sectoral composition of this effect,

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we find that wide-ranging substitution with Chinese-made machinery and electrical



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equipment as well as metal, chemical and mineral products accounts for the bulk of the

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increases worldwide, because Chinese production is on the whole more carbon-

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intensive than production elsewhere. Countries such as the USA, South Africa, Namibia

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and Iraq have experienced a decrease in emissions due to a change in production recipe.

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This is attributed to the use of less energy-intensive commodities by major sectors, such

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as the basic metals processing, electricity, gas & water and transport sectors of these

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economies. For example, the USA’s domestic production recipe changes have reduced

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carbon emissions through improvements in the material efficiency of basic metal

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refining and processing industries60, as well as in the textile and wood-to-paper supply

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chains. However at the same time, the USA has substituted Chinese for Japanese imports

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of telecommunications and sound equipment, electrical machinery and appliances, and

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office equipment, thus offsetting the domestic reductions somewhat.

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In a nutshell, we show that emissions for the 20-year period from 1990-2010 are

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primarily driven by an increase in per-capita consumption, aided by changes in

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population, production recipes, final demand composition and final demand destination.

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We show that carbon efficiency is the only driver that has resulted in a decrease in

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emissions. These findings are largely consistent with those presented by A&D21. Since

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our detailed MRIO database contains data for a longer time series and 186 countries, we

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are able to capture the change in emissions for every individual country.

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3.2 Trends in fuel-specific CO2 emissions

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Undoubtedly, CO2 emissions have increased over the last 20 years. In Section 3.1 we

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provide results for the decomposition of the change in emissions from the combined

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effect of all fuel types, primarily coal, petroleum, natural gas and biomass. Here, we

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examine each of these fuels separately, and discuss the fuel-specific decomposition of

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changes in the world’s CO2 emissions from 1990-2010 (Figure 3). This decomposition

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lets us unveil fuel-specific trends, such as an increase or a decrease in the consumption

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of a specific fuel by a country, or substitution of one fuel for another. In general, the fuel-

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specific decomposition graphs demonstrate an increase in emissions for almost all fuel

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types (Figure 3). Overall, the 10.8 Pg increase in CO2 emissions between 1990 and 2010

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constitutes emissions from Coal (+5.26 Pg), Petroleum (+2.67 Pg), Natural gas (+1.82

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Pg) and Biomass (+1.03 Pg). We start our discussion by taking the case of emissions

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from coal use from 1990-2010.

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Coal is primarily used for the production of electricity and/or heat. The emissions from

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coal use have decreased for a number of countries such as Canada, Germany, the United

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Kingdom and Spain, to name a few. Canada is an interesting case as the country’s

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economy has largely shifted towards petroleum and natural gas61. Ontario, a Canadian

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state, was once the largest consumer of coal, however has now completely stopped coal-

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fired power generation62. A shift from coal to natural gas has primarily been driven by

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policies aimed at reducing CO2 emissions, and closure or retrofitting of most coal-fired

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power stations61-62. In the same vein, emissions from coal use have decreased in

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Germany owing to the country’s efforts to diversify its fuel mix63. The United Kingdom

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(UK) requires special mention for divesting from coal-fired electricity generation. A



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number of reasons have been suggested for this shift, such as privatisation of the

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electricity industry, policies aimed at decarbonising UK’s electricity generation and

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subsidies provided to UK’s power stations (for example, Drax – the largest coal-fired

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power station in Yorkshire, England) to replace coal with biomass64. On the other end of

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the spectrum, emissions from coal consumption have increased for many of the world’s

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nations. Australia stands out in particular, since the nation has seen a steady rise in the

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production of coal from 1990-2010, mainly for exports. Australia is the biggest exporter

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of coal in the world65. Furthermore, more than 70% of electricity in Australia is

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generated using coal 66.

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The emissions from petroleum use have increased from 1990-2010, except for some

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countries such as Italy, Germany and Japan. Italy has decreased its reliance on oil, and

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has increased its natural gas consumption. An increase in taxation on oil by the Italian

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government has also been suggested as one of the reasons for a decline in oil

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consumption67. Japan has decreased its consumption of oil due to improvements in

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energy efficiency and a change in fuel mix63a, interestingly by increasing the

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consumption of coal. Noticeably, the European countries marked green for emissions

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resulting from coal and oil consumption show an increase in emissions due to shift

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towards natural gas use (Figure 3). This is essentially because of an increased push to

429

shift energy production from more carbon-intensive fuels such as coal and oil to less-

430

carbon intensive alternatives, primarily natural gas.

431

432

433


Page 18 of 32

Page 19 of 32

434


3.3 Drivers of fuel-specific CO2 emissions

435 436

In addition to decomposing the change in emissions for each of the fuels, we examine

437

the drivers of each fuel type separately (Figures S1.1 – S1.12). Our detailed examination

438

of the determinants of change reveals that emissions from coal use have decreased for

439

all nations from 1990-2010 (Figure S1.1), primarily due to improvements in the

440

emissions intensity of coal 68. A close examination of Figure S1.7 reveals that emissions

441

from the use of petroleum, natural gas and biomass have increased in South Africa. The

442

country essentially imports 95% of crude oil from Middle Eastern and other African

443

nations69. Majority of African refineries are inefficient and out-dated. A commonly used

444

benchmark measure termed the Solomon’s Energy Intensity Index (EII) is used to

445

benchmark the performance of refineries against other similar-sized refineries. In terms

446

of energy efficiency, African refineries perform poorly, and are ranked within the

447

bottom 25% of the refineries around the globe on the EII scale. The age (40+ years) of

448

the refineries is a key reason for the poor performance of African refineries, and also the

449

use of inefficient production processes70.

450

The use of natural gas and biomass has increased from 1990-2010 for many nations,

451

African nations being one of them. Natural gas production in Africa has increased two-

452

fold from 1990-201071. In the same vein, Peru has shifted towards natural gas-fired

453

electricity generation, primarily due to the rapid expansion of the Camisea Gas Project.

454

The project was initiated in 2004, and has been growing ever since. Natural gas

455

production in Peru has increased from 12 Billion cubic feet (Bcf) in 2000 to 431 Bcf in

456

201372. Vietnam is another case where natural gas production has risen in the past

457

decade, most notably a seven-fold increase from 2001 to 2012. The country is largely



458

self-sufficient in natural gas; however PetroVietnam – the key oil and gas regulator of

459

the country predicts a gap in supply and demand of gas in the coming years73. Oman’s

460

greenhouse gas emissions in the past decade were primarily caused by the flaring of

461

natural gas, however year 2012 onwards the Oman government has taken steps to

462

reduce emissions caused by flaring74. Emissions from burning biomass have increased

463

for China, certain African and European countries. China stands out in particular, where

464

CO2 emissions primarily result from the burning of biomass for fuelling rural household

465

stoves75.

466

Interestingly, a change in production recipe has resulted in an increase in emissions in

467

India and China for all four fuel types. As mentioned in Section 3.1, Indian and Chinese

468

production processes are on the whole more emissions intensive than the ones

469

elsewhere. Furthermore, emissions in these countries from coal burning are much

470

greater than those from burning the other three aforementioned fuels (Figures S1.2 and

471

S1.8). Emissions from petroleum and coal use for transport and electricity generation,

472

respectively have increased in the African continent due to rapid economic growth in

473

the past decade76 (Figures S1.3 and S1.9). Changes in the consumption vs. investment

474

balance from 1990-2010 has led to an increase in emissions in China, Canada and

475

Australia (Figure S1.4 and S1.10). Australia needs special mention as the country

476

experienced a mining boom from 1990-2010, where the investment in the mining

477

sector more than quadrupled leading to an increase in job opportunities and

478

development of many business and service industries77. Affluence and population

479

growth (Figures S1.6, S1.7, S1.11 and S1.12) have led to an increase in emissions from

480

all fuels, and for nearly all countries as explained in Section 3.1. Russia is a classic case


Page 20 of 32

Page 21 of 32


481

where the country has experienced a decline in population because of high adult

482

mortality, low fertility and failing health care system78 (Figures S1.6 and S1.12).

483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505



Page 22 of 32

506 507

4. Conclusions

508

Global CO2 emissions from fuel burning increased by 10.8 Peta-grams (Pg) from 1990-

509

2010, mostly due to the consumption of coal (49%), petroleum (25%), natural gas

510

(17%) and biomass (9%). This rise in emissions was largely driven by affluence

511

(consumption per capita) and population growth, aided by changes in production

512

structure of industries, consumption baskets of households and shifts in the

513

consumption vs. investment balance. Although supply-side measures (such as shifts in

514

fuel for power generation from coal to gas and energy efficiency of vehicles and

515

industrial processes) did offset some of this growth, emissions still continued to

516

rise, indicating that

517

emissions at sustainable levels. The literature on policies aimed at reducing

518

emissions recommends devising both demand- and supply-side measures for achieving

519

emission reductions79 . Whilst supply-side measures abound and are exploited already,

520

there is a near-absence of demand-side measures for targeting unsustainable consumer

521

demand. This can be ascribed to a number of reasons. First, it has often been suggested

522

that technological developments can lead to significant improvements in energy use

523

efficiency, thus lowering carbon emissions in general, and decoupling economic growth

524

from environmental degradation80 . Second, it is not only difficult but also impossible to

525

implement policies for interfering in people’s freedom of choice, and restraining their

526

consumption, particularly in liberal-democratic societies of most developed nations56.

527

In principle, a shift towards a ‘steady’ or ‘zero-growth’ economy would be effective in

528

reducing emissions; however, this would require societies to move away from status-

529

driven consumerism towards radical conservationism81, through broad societal

530

engagement and increased practicing of sustainable living82, whilst at the same time not

technological

improvements alone are


not sufficient to

keep

Page 23 of 32


531

compromising quality of life51, 83. Third, the presence of inherent difficulties in gaining

532

support of consumers for implementing measures to control unsustainable lifestyles

533

poses major roadblocks. This is primarily due to a weak relationship between

534

knowledge and concern for climate change, and abatement actions for mitigation of

535

impacts84. There is a wide body of evidence that suggests that consumers’ concerns for

536

climate change often do not translate to real actions. Consumers desire to be

537

environmentally friendly is often overpowered by their desire to self-indulge85. This is

538

particularly true in developed nations, where consumers have become accustomed to

539

convenient lifestyles86. Furthermore, various studies on consumer psychology and

540

behaviours indicate that there is widespread awareness of global warming and climate

541

change; however a deeper understanding of the impacts and mitigation measures is

542

missing87. Consumers often lack deeper response knowledge, thus hindering concerted

543

efforts to reduce CO2 emissions84. The results of this study offer evidence for a need to

544

tackle unsustainable over-consumption patterns, and are vital for informing future

545

policy decisions for mitigating climate change.

546 547 548 549 550 551 552 553 554 555



556

Supporting Information

557

Additional figures on fuel-specific carbon emissions.

558

559 560

Acknowledgements

561

This work was financially supported by the Australian Research Council through its

562

Discovery Projects DP0985522 and DP130101293. The authors thank Sebastian

563

Juraszek for expertly managing our advanced computation requirements and Charlotte

564

Jarabak for help with sourcing of data.

565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582


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583

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803 804

Figure 1: Global carbon footprint. Decomposition of changes in the world’s carbon

805

emissions between 1990-2010 into six key determinants. The contribution of each

806

driver as a percentage of the total change for each country is colour-coded, as either an

807

accelerating (red) or a retarding (green) effect. For example, the decomposition of

808

changes in Germany's carbon footprint between 1990-2010 yields -1.15 Pg (49.7%) of

809

emissions due to carbon efficiency, 0.79 Pg (34.2%) due to affluence, 0.06 Pg (2.5%) due

810

to final demand destination, 0.01 Pg (0.2%) due to final demand composition, 0.27 Pg

811

(11.6%) due to production recipe and 0.04 Pg (1.8%) due to population. Effects add up

812

to 0.01 Pg (100%).

813 814 815 816 817 818 819 820



821

822 823 824

Figure 2: Affluence vs carbon efficiency. The figure shows the relative change

825

(percentage increase or decrease) in carbon dioxide emissions due to carbon efficiency

826

and affluence for the time period 1990-2010. Relative increases in carbon dioxide

827

emissions due to affluence (marked red) are larger than reductions due to carbon

828

efficiency (marked green). Country acronyms: India (IND), South Africa (ZND), China

829

(CHN), Brazil (BRA), Russia (RUS), Australia (AUS), USA, Japan (JPN), Germany (DEU),

830

France (FRA) and United Kingdom (GBR).

831 832 833 834 835 836 837 838


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839

Coal

Petroleum

Natural gas

Biomass

840 841

Figure 3: Fuel-specific global carbon footprint. Fuel-specific decomposition of

842

changes in the world’s carbon emissions from 1990-2010. The total change for each

843

country is colour-coded, as either an accelerating (red) or retarding (green) effect. The

844

scale shows the values in log10 of emissions expressed in Gigagrams (Gg).



44x23mm (600 x 600 DPI)


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Trends in Global Greenhouse Gas Emissions from ... - ACS Publications

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