Underlying Emission Drivers

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What drives emissions of greenhouse gases in cities? Different academic lenses see these drivers differently. System analysts prefer to identify and quantify the immediate and proximate drivers, and assign them their respective share of the emissions problem. They typically look at contributions of various energy sectors such as residential, commercial, industrial, and transport sectors and the energy mix. Some follow broad aggregation methods using the Kaya Identity - emission is a product of a number of a decomposed factors (see Box 7.1). Economists see the energy use and emissions in income linkages, elasticity

2 China, learning and inspired by Japanese and German recycling-related initiatives, is promoting a Circular Economy (CE) initiative.

Box 7.1.

Different approaches to explaining emission drivers

Emissions

= sum (sectoral and subsectoral disaggregations)

Emissions

= f (emission/energy, energy/income, income/person, population)

Emissions

= f (economic growth, price signal, externality handling, market

mechanism)

Emissions

= f (population, organization, environment, technology, institutions,

culture)

Emissions

= f (mobility, shelter, food, lifestyle)

of demand, market role, price signals, drivers that act in such a market, and the handling of externalities. Social scientists see such drivers from a broader perspective, based in the evolution of the social system itself (Canan and Schienke 2006; Scholz 2006). To help create a balanced understanding, we mention a few major drivers propounded by these groups that reasonably describe the key drivers following the author's previous work (Dhakal 2004) on such drivers in Asian megacities.

Urban demography: Urban demography can affect emissions through size, growth, composition and distribution of population. While urban population growth has levelled off in many developed countries, and indeed is declining in some European cities, it continues to rise in the rapidly developing and least developed cities in the developing world (UN 2006). High population growth means bigger population size, and such population size, combined with other factors, becomes a direct measure of the emissions volume of a city. However, since household size has an important bearing on energy uses, such that per capita energy use in smaller size households is expected to be different from bigger size households, the number of households provides a better indicator than population itself in a city. In Japan, the 2006 Annual Report of the Ministry of the Environment shows that per capita energy use in a one-person household is about 17 GJ/person/year in comparison to about 11 GJ/person/year in a four-person household (MOEJ 2006a). Similarly, it shows that the amount of household waste, which is a good proxy of consumption in a one-person household, in 2003 was 941 grams/person/day compared to 440 grams/person/day in a four-person household in Kawasaki City. We do not have empirical evidence to show that it prevails only in the same income groups or across different income groups, or whether this is due to the fact that smaller households tend to be more affluent and hence more wasteful or if it is due to the scale effect that leads to reduced per unit impact.

When it comes to population composition, the exact impact of different shapes of urban population pyramids3 to emissions is largely unknown, and how different households with different age structures use energy differently in various settings is also an open question. At the national scale, developed countries have a higher share of senior citizens - the aging society syndrome - but whether this can be generalized to cities is unknown because young people often move to cities for better opportunities, leaving an increasingly aging population in small towns and the rural areas. Japan is one example of this trend. In general, in developed cities, senior citizens are expected to use more energy per capita and in a society

3 A distribution of male and female population by age groups with population in X-axis and age group in Y-axis.

of nuclear families it might be easier to see that effect than in others. More than age structure, the population difference from daytime to the night-time has a well-established bearing on emissions. The Tokyo population, for example, has stabilized since the early 1970s, and the population of the 23 wards constituting downtown Tokyo is decreasing, but Tokyo attracts one third of its total workforce from surrounding cities and prefectures utilizing its well-developed surface rail and subways. The ratio of daytime to night-time population was 1.25 in 1999 in Tokyo; in the 23 wards, the ratio is as high as 1.41 (TMG 2000).

Economic development: As mentioned above, economic growth has a very clear relation to emissions because higher growth usually requires more use of energy, and post-fossil fuel sources are still vastly underused. Economic growth has been regarded as the most influential factor for the rise in emissions in Tokyo, Seoul, Beijing and Shanghai over the last three decades (Dhakal 2004). It is also a key driver in Mexico City, Mendoza, Buenos Aires, and Santiago (Romero Lankao et al. 2005). However, the extent of the influence of economic growth on emissions depends also on the structure of the economic activities prevailing in a city. If a city is industrial in nature and is dominated by primary and secondary industries, the emission intensity of the economic activities will be higher such as in Beijing, Shanghai, Ho Chi Minh and so on. However, if the city is heavily commercial in nature, dominated by service industries, the direct emission intensity of its economic activities is lower such as in Tokyo, and perhaps Seoul. Does slow or negative economic growth result in slowing or reducing emission growth to the same extent as income does? We do not have published data to answer this question with quantifiable precision but studies in Tokyo and Seoul have shown that there are time lags between the slowing of economic activities and the slowing of emissions, and the extent of reductions in emissions may be smaller than the extent of reduction in economic growth (Dhakal 2004). In other words, within commercial or service-oriented cities, economic failure may not lead to an equivalent drop in emissions although its indirect impact on the embodied carbon stream would be larger because economic activities in such cities are not energy intensive. Along the same lines, it remains to be seen whether a rise in economic growth leads to a proportional rise in emissions, but it is assumed that this will not be the case since technological improvements and diffusions lessen environmental impacts over time. The learning effect prevails not only for technology but for society at large. Decoupling economic growth from emissions is a key issue among scientific and policy communities worldwide and this debate needs to be accelerated in the attempt to find options that will lead to low carbon societies.

Infrastructure and technology: Energy demand itself is use derived; urban residents do not demand energy directly but they procure and use services such as lighting, heating, cooling, motive power and so on. In the energy literature, a clear distinction has been made between service demand, useful energy demand, end-use energy demand, secondary energy demand and primary energy demand. There are a number of different pathways through which a particular service demand can be met. Since the emission impacts of different energy pathways are different, choice of such pathways determine the extent of emissions. Dhakal (2004) describes one such example in which the per capita energy consumption of Tokyo, Beijing, Seoul, and Shanghai are converging over time but their per capita emissions are diverging due to rising energy use, improvements in energy efficiency, and the coal dependency of the energy system in Beijing and Shanghai (Fig. 7.2). Urban physical infrastructure and technology play crucial roles in shaping such alternative emission pathways. They are related to energy supply and choice, buildings, modes of mobility, and energy and process efficiencies of technologies in household, commercial, transportation and industrial sectors in a city. Tokyo has lower CO2-equivalent per capita emission levels

1960 1970 1980 1990 2000 2010 Trends in per capita energy consumption

Fig. 7.2. Per capita energy and emissions in Tokyo, Beijing, Seoul and Shanghai. Source-. Dhakal (2004).

10.00

0.00

1960 1970 1980 1990 2000 2010 Trends in per capita energy consumption

Shanghai Beijing

Seoul C Tokyo r

] 6.90 Shanghai

tJĀ» Beijing

Seoul C Tokyo r

Seoul

Seoul

1960 1970 1980 1990 2000 2010 Trends in per capita emissions

Fig. 7.2. Per capita energy and emissions in Tokyo, Beijing, Seoul and Shanghai. Source-. Dhakal (2004).

3.76

than Beijing and Shanghai, due to its efficient urban infrastructure, greater reliance on somewhat lower emitting sources, notably nuclear power, natural gas and hydro-electricity, and more efficient end-use technology, in addition to other factors (Dhakal 2004). If a city draws energy from coal, the city does not perform well in terms of emissions. A city that heavily relies on private cars and has an underdeveloped or underutilized mass public transport system is not emission friendly. In commercial and residential buildings, the insulation of the buildings, the efficiency of heating and cooling equipment, the type of lighting, and the efficiency of appliances determine the extent of energy use. Urban residents' desire to take services from an efficient infrastructure leads to low emission pathways. In many cities, the rate of private motorization is becoming an increasingly serious problem. In denser and bigger cities in the developing world, industrial relocation is pushing primary and secondary industries beyond the city limits over time and thus motorization is outpacing other sectors for emissions (Dhakal and Schipper 2005). Continuing economic growth, rising motorization and the lack of efficient and adequate public transport systems are causing a bleak emissions outlook for rapidly developing cities in Asia, Latin America, and Africa.

Urban form and function: Urban forms are an important determinant of emissions. City size, density, shape and distribution of functions affect emissions through setting the conditions under which infrastructure and urban services are demanded, provided and consumed. The compactness of urban settlements influences the demand for energy for transportation and other areas such as district heating and cooling using co-generation systems. Asian cities, in contrast to European and North American cities, are continuously getting denser while expanding - starting from a less dense base. Because many North American metropolitan areas are low in density and sprawled over large areas they require large amounts of energy to run their transportation and distribution systems; their public transportation is not cost effective. Urban sprawl, in which low density suburbs depend on extensive distribution systems, undermines efficient energy use. Mixed land use results in different energy use than does segregated land use. Urban zoning and industrial relocation from city centres to peri-urban areas in cities significantly influence travel demand and energy use. A combination of mixed land use and high density self-reliant settlement clusters served by efficient mass transportation is generally regarded to help make a city emission reducing, particularly if further opportunities existed to use non-motorized modes, and pedestrian friendliness in those clusters was assured. In essence, determining precisely which urban forms are emission friendly is a difficult question to answer. Given the widespread failure of cities to successfully control development through planning across many developing countries, identifying what opportunities do exist to restructure urban forms and functions is a critical challenge.

Behavioural and societal factors: Cultural and social contexts shape the behaviour of urban residents towards carbon emission reducing pathways, in combination with the supply-side conditions, such as infrastructure, technology, urban forms, etc., that are imposed on the urban system. Choosing cars with smaller engines, opting for fuel efficient cars, rational use of energy (switching lights off when not needed in homes, using air conditioning and heating moderately), maintaining greenery around houses, combating mass consumerism by living a more self-sufficient life are results of behavioural changes that are subject to public policy and civic leadership and affect energy use and emissions levels positively. One example of behavioural dogma in need of adjustment is the fact that a car is promoted and perceived as a symbol of social class and lifestyle status in many developing countries, and typically associated with city life and achievement. How precisely such behaviour is guided is little studied today, and what actions are likely to induce positive behavioural changes are equally difficult to ascertain. By and large, the prevailing social value system affects behaviour; many public efficiency campaigns bank on the assumption that awareness, education, correct information, and proper incentives can induce positive behaviour.

Globalization: Not many past studies have examined whether rapid globalization is a key factor in emission growth, but it is plausible, even self-evident. Globalization is taking place along economic and cultural fronts and is shaping the conditions under which the physical infrastructure of cities and the behaviour of urban residents are developed. Foreign direct investments and trade agreements affect the location and technology of manufacturing and commercial activities and labour reorganization (Romero Lankao et al. 2005). The prevalence of too many multinational corporations and their strong political leverage may create a situation where carbon relevant decisions cannot easily be locally decided or controlled. Romero Lankao et al. (2005) show that due to strong lobbying by the automobile industry, emission regulations in Mexico have lagged far behind federal regulations in the United States. In China, individual cities are competing with each other to attract foreign direct investments and are compromising their local environmental conditions and tax policies (Dhakal 2005). Such competitions are also observed in other countries such as Vietnam and India. In addition to economic globalization, global media such as television and the internet increase global connectedness and influence individual choices and behaviour. This is not to say that globalization in itself is either detrimental or beneficial to efforts to stem emissions but also illustrates that it is an important player in a variety of direct and indirect ways.

Institutional and political factors: Beyond any doubt, the role of political and institutional factors is important for emissions as well as mitigation. Not only decision-making in cities but also provincial and national decision-making processes and actors affect emissions from cities because a city is governed in a complex way by many different layers of governments. For urban environmental issues, in most Asian cities, municipal governments manage solid waste while only in relatively big cities in a few countries do municipal governments substantially manage air pollution (Dhakal 2005). Who makes the policies? Are the policies well consulted and applied? Are policy instruments reasonably robust? Are the implementing authorities well resourced? These questions are important. Decentralization of responsibilities to local agencies without adequately developing their capacity only defers the responsibility. Therefore, a multi-scale style of governance with clearly defined institutional mechanisms is propounded for issues like greenhouse gas emissions (Gustavsson et al. 2006; Bulkeley and Betsill 2005).

Natural factors: We cannot discount the natural phenomena of cities. Climate factors directly affect energy use, and thus emissions, due to greater demand of heating or cooling services. A city in the tropics uses more air conditioning energy. High latitude cities in Northern Europe, North America, China, and Mongolia, for example, need more heating energy during the winter than cities in temperate climate zones. A coastal city could be more vulnerable to the impacts of climate change than others.

Above, we discussed many drivers that influence emissions from a city. The impacts of some of these drivers on emissions are well established while others are not clear. In reality, a number of such drivers act collectively on an urban system and we often tend to miss existing inter-relations among drivers and rebound effects of interventions in those drivers in a larger context. This is one reason why devising policies for urban carbon management becomes complex. In addition, we do not know clearly what combination of these drivers applies to different city types - by size, function, geography, income, etc. - and what different clusters of drivers lead to a particular defined emission pathway. This leads us to the next point, which is to identify which of these drivers is most important. We leave this unanswered question to future researches through further examination of the facts.

Before we conclude this section, it is important to discuss the complexities involved in comparing carbon emissions among cities. It is not possible to say that one city is doing better than another simply by comparing the volume of carbon dioxide emission given their local situations. Emissions per capita is a better indicator than emissions alone because it internalizes some of the equity debates. This chapter proposes a framework of indicators to assess a city's carbon policy friendliness through the collective effect of the rate of change in emissions per unit economic activity and the rate of change in emissions per capita scaled by appropriate climate factor (Fig. 7.3). In Dhakal et al. (2002), we outlined a graph where the X-axis is emissions per capita over time and the Y-axis is emission intensity of economic activities over time plotted for a few Asian megacities. The direction, vertical gradient and horizontal gradient of the graph over time show the performance of a city over time. However, we could not scale for climate factors at that time. While this

Emission per unit economic activity in kg/$PPP

- General direction of graph (towards right: unfavourable in general; towards left: favourable; downward: favourable; upwards: unfavourable)

What to note in the figure:

Per capita ^ emissions in kg/person

- Vertical gradient of graph

(speed of carbon intensiveness of economic activities)

Climate scale

- Horizontal span of graph (speed of rise or fall in per capita emissions)

Fig. 7.3. Comparing cities' carbon performance over time.

framework has yet to be tested in quantitative terms, we believe it is a better indicator than others for now.

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