Scenario Analysis of Low-Carbon Urban Energy System in Asian Cities

  • Hooman Farzaneh


The main challenges of the coming urban energy transitions in Asia include increased urbanization in developing countries, climate change-energy security imperatives, and new technologies at local and grid levels. These challenges highlight the need for Asian cities to reconsider how new urban investments should be prioritized in order to reduce resource consumption and emissions, as well as to achieve local and national development goals. This chapter will explore the application of a quantitative bottom-up modeling approach for the assessment of multiple benefits of climate change mitigation in Asian cities. Testing the model in Delhi shows the potential environmental (GHG mitigation and air quality) and health co-benefits of developing a clean transport scenario for this city. The results reveal that the implementation of the strategic plan in Delhi’s urban transportation system has GHG emission reduction potential of about 4.3 million tonnes of CO2 with an aggregated reduction of about 0.2 million tonnes of other local air pollutants, which could prevent about 22,000 cases of mortality and brings more than USD 1850 million per capita by 2030.


Climate change Urban energy Energy modeling Asian cities 

1.1 Introduction

The main challenges of the coming urban energy transitions in Asia include increased urbanization in developing countries, climate change-energy security imperatives, and new technologies at local and grid levels. These challenges highlight the need for Asian cities to reconsider how new urban investments should be prioritized in order to reduce resource consumption and emissions, as well as to achieve local and national development goals. A number of factors influence energy use in and the resulting greenhouse gas (GHG) emissions from these cities. The major ones include:
  • The urban spatial structure

  • The nature of transportation systems, income, and lifestyle

  • The energy efficiency of key technologies, industrial processes, and building technologies

  • Climate and waste disposal methods

Cities in rapidly industrialized regions of Asia face many tasks related to economic and environmental issues. In megacities such as Beijing and Shanghai, industries consume more than half of the total energy use, reflecting the fast growth of Chinese economy, while in large cities of countries whose economies are growing at a slower pace, it is the transportation sector which consumes more than half of the total energy. Industry and power generation are major contributors to the carbon footprint of Chinese cities. Meanwhile, residential and commercial buildings account for more than half of the energy consumed in cities such as Tokyo and Seoul.

Urban authorities are largely not aware of the multiple benefits of energy management and GHG reduction. Given their growing scale and significance, Asian cities will have to be active in the global fight against climate change if it is to be effective. Municipal authorities in Asian cities therefore have a significant scope to pursue urban low-emission strategies and clean energy initiatives in ways that will also foster economic development. Moreover, clean energy initiatives at the city scale could generate knowledge and innovations that can have wider economic and social benefits, in addition to inspiring climate action in other cities and at a national scale. Without more coordination between international, national, regional, and local institutions, integration into different sectoral priorities and policies, and engagement between the public, private, and civic sectors, it seems likely that the cities in Asia will lock in more fully to high-cost, high-carbon development paths. Because of the global significance of Asian cities, policies, and programs, facilitating large-scale adoption and deployment of clean and renewable energy will need to play a central role in this area.

Many local governments in Asian cities face a dual challenge in achieving top-priority local development goals, such as improving standards of living through extending access to modern energy and increasing employment while also supporting national climate change action. To support broader development goals while also reducing GHG emissions, a number of governments are developing and implementing LEDS (low-emission development strategies) which aim to achieve development priorities with minimal GHG (greenhouse gas) emissions as part of their national objectives. Historically, literature on evaluating the impacts of a shift to a low-emission pathway has focused on the costs, but in fact, the benefits may outweigh the costs when considering broader impacts (e.g., public health). By including the broader set of benefits in the cost-benefit analyses conducted during planning processes, local governments get more comprehensive assessments of their potential LEDS investments. Following APN’s Fourth Strategic Plan (2015–2020) (APN 2015), this chapter aims to develop effective science-policy interaction to address the opportunities where LEDS can be used to support energy system and environmental and/or economic development planning strategies across the Asian region.

Different institutions and organizations have a different understanding, definition, and interpretation of benefits assessment of LEDS. For instance, the “co-benefits” is defined by the MOEJ (Ministry of the Environment of Japan) and Intergovernmental Panel on Climate Change (IPCC) as the process of controlling GHG emissions and reducing other local emissions (e.g., SO2, NOx, CO, and PM); on the other hand, local pollution control in the sustainable development process can also reduce or absorb CO2 and other GHG emissions (Smith et al. 2014; MOEJ 2008). The varying use of this term in “Climate co-benefits” and “Climate and air co-impacts” indicated that there is almost no agreement on assessing co-benefits with diverse methods and tools. Some studies made in the similar research area mostly focus on qualifying the co-benefits of mitigating GHG emissions and reducing air pollutants through policies of energy conservation, climate change, and air pollutant control (Farzaneh 2016, 2017a, b).

The multiple benefits assessment which will be discussed in this chapter is far beyond a simple co-benefits approach and will refer to the achievement of mitigating climate change, solving local environmental and developmental problems, as well as improving public health and local economy through the implementation of LEDS in urban area. This chapter will demonstrate a new strategic planning mechanism for achieving multiple energy, environmental, public health, and economic benefits of clean energy development strategies in Asian cities, together with a robust analytical framework that can be used to assess those benefits during the development and implementation process. By evaluating potential clean energy policies with criteria that cut across the multiple benefits, localities are able to select options that facilitate the achievement of multiple goals and avoid options that may impede key priorities.

1.2 Integrated Analytical Framework

The term “multiple benefits” is effective because it emphasizes an integrated approach, linking climate change mitigation to the achievement of sustainable development in the economic systems. The broad concept of multiple benefits assessment in the urban energy systems is gaining traction worldwide and consequently leading to changes in the governance of cities. Assessing the multiple benefits of LEDS is usually based on conclusions of many concepts and theories from different scientific disciplines. In this investigation, in order to be able to quantify the multiple benefits (energy, environment, health, and economy), the concept of LCS (Low-Carbon Society) has been used (Farzaneh 2017a, b). Figure 1.1 describes the interaction between the design of the energy system considering specific targets and the society which demands energy to function in a sustainable way (Nakata et al. 2011). It can be explained through proposing the LCS concept.
Fig. 1.1

Application of the LCS concept to an urban energy system

The implications of the LCS vision in the present society must balance the factors related to the 3Es. Analysis of multidimensional interactions between 3Es and the urban energy system is a complex task that necessitates the development and utilization of analytical tools. To address this complex issue, in this research a city-level CGE (computable general equilibrium) model has been developed on the basis of the general equilibrium theory. It uses actual economic data from a SAM (social accounting matrix) which is an accounting framework that reflects the circular flow of city’s economic activity to estimate how a city might react to changes in clean energy policies. In this model, the expenditures and savings are the primary inputs to the subsequent analysis of macroeconomic effects on income, employment, and output (Fig. 1.2).
Fig. 1.2

Overall schematic of the CGE model (Adopted from Böhringer 2004)

The CGE model has two main parts: supply and demand. On supply side, the microeconomic principles have been utilized to develop a concept that would represent the behavior of an urban energy system in a market with a perfect competition. The local government as a decision-maker in this market strives for maximum satisfaction (or utility) of delivering certain energy service to the end users such as providing required electricity at the end-user level. The utility or satisfaction is a function of a broad range of parameters such as quality of the service, comfort, accessibility, environment, costs, and time. Maximizing utility is subject to certain constraints due to the availability of resources. The resources are time, capital for obtaining a quality service, availability of reliable system environment, and income. The solution of such a mathematical model would be possible if the utility function could be identified and formulated explicitly based on both supplier (local government) and consumer (end users) viewpoints. An alternative methodology has been developed which may be categorized as a direct solution of the model. Although the solution of the model based on the maximization of the utility of delivering energy services would be hardly possible due to difficulty in obtaining an explicit formulation of the utility function, one may make an effort of solving the dual of the primary model. The dual formulation of the primary model would achieve the optimal utility with minimum total costs, which would direct capital and operation costs. The above concept may be formulated on the basis of mathematical programming approach as given below (Farzaneh et al. 2016a, b):
$$ \operatorname{Min}\ \mathrm{TC}=\sum \limits_i{p}_i{x}_i $$
subject to
$$ f\left({x}_1,\dots, {x}_n\right)\ge {U}^{\ast } $$
$$ \sum \limits_i{x}_i\le {R}_i $$
$$ {x}_i\le {A}_i $$
$$ {x}_i\ge 0 $$
  • TC: Total cost of the system

  • xi: Determinant factor i such as energy, material, land, technology, etc.

  • U*: Defined level of the utility

  • Ri: Available resource of determinant factor i such as fossil fuel or renewable energy

  • Ai: Bound on using or consumption of factor i such as technical, environmental, institutional, and economic constraints

  • pi: Unit cost of determinant factor i (i.e., cost of technologies and energy carriers)

The level of segregation is usually determined by the ability to introduce the number of end users and different technologies which are used to operate the flow of energy from the resource level to the end-user level. On demand side, on the other hand, end users are divided into buildings, transport, and industrial sectors. A spreadsheet simulation model based on bottom-up end-use method and the Avoid-Shift-Improve (A-S-I) approach has been applied to the end-user level in order to assess the effect of different scenarios of socioeconomic, technological, and demographic developments on energy consumption and emissions of the citywide energy system in a multi-sectoral context (Farzaneh et al. 2014). The model systematically relates the GHG and air pollution emissions based on the specific energy demand in the end-user sectors in cities to the corresponding social, economic, and technological factors that affect this demand. The nature and level of the demand for energy are a function of several determining factors, including population growth, number of inhabitants per dwelling, number of electrical appliances used in households, local priorities for the development of certain economic sectors, the evolution of the efficiency of certain types of equipment, penetration of new technologies or energy forms, etc. An understanding of these determining factors permits the evaluation of the various categories of energy demand for the urban energy system considered. The total energy demand for each end-use category is aggregated into three main energy consumer sectors. Application of the model is subject to the identification and estimation of the performance function of the urban energy system which is possible by segregating the whole energy system into incremental elements such as end user, final energy, energy conversion, and energy resources. When various energy forms, i.e., electricity, fossil fuels, etc., are competing for a given end-use category of energy demand, this demand is calculated first in terms of useful energy and then converted into final energy, taking into account market penetration and the efficiency of each alternative energy source and using new technologies. Demand for fossil fuels is therefore broken down in terms of coal, gas, or oil, and the substitution of fossil fuels by alternative “new” energy forms (i.e., solar, wind, etc.) is estimated, due to the importance of the structural changes in the urban energy system that these energy forms may be introduced in the future. Since these substitutions will be essentially determined by policy decisions, they are to be taken into account at the stage of formulating and writing the scenarios of development. The scenarios can be subdivided into two categories:
  • One related to the socioeconomic system describing the fundamental characteristics of the social and economic evolution of the urban energy system such as lifestyle changes, population growth, and GDP growth

  • The second related to the technological factors affecting the calculation of energy demand, for example, the efficiency and penetration potential of each alternative energy form and new technology such as smart grid

Following this approach, the planner can make assumptions about the possible evolution of the social, economic, and technological development patterns of the local energy system that can be anticipated from current trends and governmental objectives. The methodology comprises the following sequence of operations:
  • Desegregation of the total energy demand of the city into a large number of end-use categories in a coherent manner

  • Identification of the social, economic, and technological parameters which affect each end-use category of the energy demand

  • Establishing in mathematical terms the relationships which relate energy demand and factors affecting this demand

  • Estimation of the energy demand-related GHG emission and air pollution from different sub-sectors

  • Developing (consistent) scenarios (policy interventions) of social, economic, and technological development for the given city’s energy system

  • Evaluation of the climate co-benefits resulting from each scenario

  • Selection among all possible scenarios proposed, the “most probable” patterns of development for the city through analyzing CBA and system sustainability

Assessing the public health benefits of clean energy development in the selected cities would be possible through selecting of concentration-response (C-R) functions. For most of the health effects include premature mortality and exacerbation of health conditions such as asthma, respiratory disease, and heart disease, a variety of alternative C-R functions have been collected from epidemiological research. In this model, using the source-receptor transfer matrix (SRTM) enables us to evaluate the effects of avoided emissions of PM, SOx, and NOx (e.g., in units of tons) on their concentrations (e.g., in units of μg/m3 or ppm). The SRTM is a reduced-form model based on a standard Lagrangian dispersion model designed for short-range (up to 10 km) dispersion in selected areas (i.e., road transport, waste disposal site, etc.). The C-R functions have been used to link the estimated changes in concentrations to a number of health endpoints. Finally, economic values for each health effect will be derived from economic literature and can be carefully matched to the types of avoided health effects estimated in this analysis. Figure 1.3 shows the integration between the CGE model and public health co-benefits assessment model.
Fig. 1.3

Integrated energy-health impact assessment

The CGE model is implemented as a mixed integer-linear programming problem using the GAMS (General Algebraic Modeling System) to find the minimum total cost of delivering a certain level of energy service through the optimal combination of available technologies and resources in the urban energy system.

1.3 Application of the Model to the Study Area: Delhi Clean Transport Scenario

Delhi’s transportation sector is the largest consumer of energy and represents a major contributor to GHG emissions and local air pollution. This sector is expected to experience a large increase in fossil fuel consumption resulting from the fast growth of private vehicles. Delhi already has exceptionally high levels of private car use with around two million cars in the city (Farzaneh et al. 2016a, b). The city also experienced rapid expansion of demand in urban transport which has led to transportation networks with high traffic volumes of private transportation modes and congestion, which has resulted in adverse health effects such as respiratory and heart diseases. Public transport in Delhi is currently dominated by buses, but the recent construction of a metro system has attracted much attention as a solution to Delhi’s transport problems. Along with the expanding population and intensified urban development, the projection of the travel demand and its related energy consumption is represented in Fig. 1.4.
Fig. 1.4

Projection of travel and energy demand in Delhi’s transport sector

The demand for energy in this sector will experience a massive increase from 175 PJ in 2010 to 315 PJ in 2030, which would be dominated by gasoline fuel. Depicted in Fig. 1.5, the total GHG emissions show an increasing trend from 5.4 Mt CO2-eq in 2010 to 9.8 Mt CO2-eq in 2030.
Fig. 1.5

Projection of GHG and air pollution emissions in Delhi’s transport system

The pollutant CO has the greatest weight in the air pollution indicator, while SO2 has the lowest weight. CO emissions from the transport sector are expected to increase significantly to 1.5 million tonnes in 2030, which is mainly affected by the average age of the fleet, combustion efficiencies, and the driving strategy in different traffic conditions in the Delhi metropolitan area.

To tackle the serious challenges of air pollution, the local government in Delhi has developed a series of initiatives as follows:

1.3.1 Fuel Efficiency: Early Adoption of BSES V and BSES VI Auto Fuel Norms

BSES (Bharat stage emission standards) are emission standards instituted by the Government of India to regulate the output of air pollutants from internal combustion engines and spark-ignition engine equipment, including motor vehicles. In 2016, the Indian government announced that the country would skip the BS-V norms altogether and adopt BS-VI norms by 2020 (DieselNet 2017). By moving to BS-VI, the transport sector of the city of Delhi will use the highest specifications of fuel standard available in the world (Fig. 1.6).
Fig. 1.6

Early adoption of BS-V and BS-VI auto fuel norms

1.3.2 Battery Vehicle: Promotion of Battery-Operated Vehicles/EVs

Delhi government through Delhi Pollution Control Committee provides financial subsidy on newly purchased battery-operated four- and two-wheelers. Financial subsidy is provided by Delhi Pollution Control Committee from the Air Ambience Fund, created by levying 25 paisa per liter of diesel (DPCC 2015). Besides onetime fixed subsidy of 15,000 Rs is also provided to battery-operated e-rickshaw owners, authorized by the Transport Department and registered with registering authority of the Transport Department (Table 1.1).
Table 1.1

Financial subsidy for the battery-operated vehicles/EVs

Type of vehicles

Cost of vehicles (base price)

Subsidy given by Govt. of Delhi (in Rs)


Up to 5 lakhs



More than 5 lakhs



Up to 20,000/−






More than 25,000/−


1.3.3 Modal Shift: Increasing Ridership in Delhi Metro

With almost 23 hundred thousand passengers using the Delhi Metro network every day, increasing ridership has been the Delhi Metro Rail Corporation’s biggest challenge. Yet it’s also its biggest challenge as trains struggle to keep up with expanding ridership. At present the total ridership of Delhi Metro is estimated to be about 25%. While Delhi Metro has been trying to expand its fleet, it is currently in the process of converting six-coach trains into eight-coach ones on the main line. It has been planned in Rapid Metrorail Gurgaon with a total length of 11.7 km serving 11 stations. Based on these actions and objectives of the aforesaid initiatives (clean transport scenario), the model was used to evaluate the GHG emission reduction potential and the multiple benefits achievable by improving air quality in 2030. According to the results, modal shift from private modes to the public transport systems, including the metro, can help reduce energy consumption, CO2 emissions, and pollution load in the city of Delhi. Figure 1.7 shows the expected GHH emission reduction from the implementation of the above plans, which is estimated to be about 4.3 million tons in 2030.
Fig. 1.7

Future mobility and expected GHH emission reduction in the clean transport scenario

The high-quality public transport system in the clean transport scenario can provide additional benefits besides emission reduction, including improved public health. As shown in Fig. 1.8, the model predicts that the total amount of harmful gas emissions, such as SO2, NOX, and PM10, would decline in the clean transport scenario compared to the baseline scenario by approximately 48%.
Fig. 1.8

Expected co-benefits of local air quality improvement in the clean transport scenario

Using the mortality rates that were collected for the Ministry of Health and Family Welfare for 2008–2011 (MHFW 2018) and also annual average data for concentration of SO2, NOX, and PM10, which were derived from continuing measurement taken in the period of 2008–2011, from five monitoring stations throughout the Delhi metropolitan area, the estimated annual reductions in the mortality rate from the clean transport scenario are given in Table 1.2.
Table 1.2

Estimated annual health outcomes from the clean transport scenario in 2030

Exposure metric and subgroups

Health outcomes (deaths prevented/year)




Total mortality and short-term exposure (all ages)




Cardiovascular mortality and long-term exposure (age >30)




Respiratory mortality and long-term exposure (age >30)




Respiratory mortality and short-term exposure (age <5)




According to the results, the annual reduction of cases of mortalities varies from 19,200 (exposure to PM10) to 419 (exposure to SO2) in Delhi in 2030. The larger numbers for the projected reduction of cases of cardiovascular mortalities imply that the pollution impact on these cases is more serious than others. Among all pollutants, the reduction of PM10 plays a significant role in achieving the desired health outcome.

Implementation of the clean transport scenario in Delhi has direct and indirect impact on the local economy of this city. Direct effects are changes in sales, income, or jobs associated with the on-site or immediate effects created by an expenditure or change in final demand, for example, the employment and wages for workers who assemble batteries at a manufacturing plant. Indirect effects are changes in sales, income, or jobs in upstream-linked sectors within the region. These effects result from the changing input needs in directly affected sectors, for example, increased employment and wages for workers who supply materials to the battery assemblers. Induced effects are changes in sales, income, or jobs created by changes in household, business, or government spending patterns. These effects occur when the income generated from the direct and indirect effects is re-spent in the local economy. The cumulative growth of GDP and its absolute employment caused by the clean transport scenario in Delhi were estimated by the model which is represented in Fig. 1.9.
Fig. 1.9

The cumulative growth of GDP and its absolute employment caused by the clean transport scenario

1.4 Conclusion

Sustainable transport, particularly in developing countries, is an important element of climate change policies, which can be integrated into development objectives such as good health and well-being as well as clean energy and sustainable cities. While energy saving and controlling GHG emissions and reducing other local emissions may be a primary goal of LEDS, other benefits also accrue from these investments. In order to meet both climate protection and other human development goals, it is important to seek perceptible multiple benefits to justify interventions. In the urban transport sector, the climate change mitigation actions are usually linked with the application of clean technologies or behavioral changes by introducing affordable travel options. However, developing a low-carbon transport system can bring additional benefits beyond GHG emission reduction, such as improved air quality and public health as well as reducing traffic congestions, injuries, and noise. Therefore, analyzing the multiple benefits of climate change mitigation in the urban transport sector may be high on the agenda of important policy actors, since there is large potential to introduce the co-benefits approach into ongoing projects and existing climate change mitigation actions, as exemplified by this study in Delhi, which suffers from several social, economic, and environmental problems caused by a poor urban transportation system. This study explored the application of a quantitative bottom-up modeling approach, based on an integrated CGE approach, for the assessment of multiple benefits in the Delhi’s transport sector. Testing the model in Delhi showed the potential environmental (GHG mitigation and air quality) and health co-benefits of developing a clean transport scenario for this city. The results showed that the implementation of the strategic plan in Delhi’s urban transportation system has GHG emission reduction potential of about 4.3 million tonnes of CO2 with an aggregated reduction of about 0.2 million tonnes of other local air pollutants, which could prevent about 22,000 cases of mortality and brings more than USD 1850 million per capita by 2030.


  1. Asia-Pacific Network for Global Change Research (2015) Fourth strategic plan 2015–2020, ISBN 978-4-9902500-3-4, Kobe, JapanGoogle Scholar
  2. Böhringer C (2004) Sustainability impact assessment: the use of computable general equilibrium models. Écon Int 99:9–26Google Scholar
  3. DieselNet (2017) Emission standard,
  4. DPCC, Delhi Pollution Control Committee (2015)
  5. Farzaneh H (2016) Chapter 5: energy. In: Christopher NH Doll, Puppim de Oliveira JA (eds) Urbanization and climate co-benefits implementation of win-win interventions in cities. Routledge Taylor & Francis, New YorkGoogle Scholar
  6. Farzaneh H (2017a) Multiple benefits assessment of the clean energy development in Asian cities. Energy Procedia 136:8–14CrossRefGoogle Scholar
  7. Farzaneh H (2017b) Development of a bottom-up technology assessment model for assessing the low carbon energy scenarios in the urban system. Energy Procedia 107(2017):321–326CrossRefGoogle Scholar
  8. Farzaneh H, Suwa A, Doll CNH, Puppim de Oliveira JA (2014) Developing a tool to analyze climate co-benefits of the urban energy system. Procedia Environ Sci 20:97–105CrossRefGoogle Scholar
  9. Farzaneh H, Doll CNH, Puppim de Oliveira JA (2016a) An integrated supply-demand model for the optimization of energy flow in the urban energy system. J Clean Prod 114:269–285CrossRefGoogle Scholar
  10. Farzaneh H, McLellan B, Ishihara KN (2016b) Toward a CO2 zero emissions energy system in the Middle East region. Int J Green Energy 13(7):682–694CrossRefGoogle Scholar
  11. MHFW, Ministry of Health and Family Welfare (2018)
  12. Ministry of the Environment, Japan (MOEJ), The Co-benefits Approach for GHG Emission Reduction Projects (2008)
  13. Nakata T, Silva D, Rodionov M (2011) Application of energy system models for designing a low-carbon society. Prog Energy Combust Sci 37:462–502CrossRefGoogle Scholar
  14. Smith KR, Woodward A, Campbell-Lendrum D, Chadee DD, Honda Y, Liu Q, Olwoch JM, Revich B, Sauerborn R (2014) Human health: impacts, adaptation, and co-benefits. In: Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp 709–754Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Interdisciplinary Graduate School of Engineering Sciences, Kyushu UniversityFukuokaJapan

Personalised recommendations