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Greenhouse Gas Mitigation in Chinese Eco-industrial Parks by Targeting Energy Infrastructure: a Vintage Stock Model Yang Guo, Jinping Tian, Marian Chertow, and Lujun Chen Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b02837 • Publication Date (Web): 31 Aug 2016 Downloaded from http://pubs.acs.org on September 18, 2016
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Greenhouse Gas Mitigation in Chinese Eco-industrial Parks by
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Targeting Energy Infrastructure: a Vintage Stock Model
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Yang Guo 1, Jinping Tian 1, *, Marian Chertow 2 Lujun Chen 1, 3
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School of Environment, Tsinghua University, Beijing 100084, China
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Center for Industrial Ecology, School of Forestry and Environmental Studies, Yale
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University, New Haven, Connecticut, 06511, United States
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Zhejiang Provincial Key Laboratory of Water Science and Technology, Department of
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Environment, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, Jiaxing
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314006, China
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ABSTRACT
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Mitigating greenhouse gas (GHG) emissions in China’s industrial sector is crucial
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for addressing climate change. We developed a vintage stock model to quantify the
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GHG mitigation potential and cost effectiveness in Chinese eco-industrial parks by
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targeting energy infrastructure with five key measures. The model, by integrating
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energy efficiency assessments, GHG emission accounting, cost-effectiveness analyses,
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and scenario analyses, was applied to 548 units of energy infrastructure in 106 parks.
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The results indicate that two measures -- shifting coal-fired boilers to natural gas-fired
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boilers and replacing coal-fired units with natural gas combined cycle units -- present a
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substantial potential to mitigate GHGs (42%-46%) compared with the baseline
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scenario. The other three measures -- installation of municipal solid waste-to-energy
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units, replacement of small-capacity coal-fired units with large units, and
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implementation of turbine retrofitting -- present potential mitigation values of 6.7%,
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0.3%, and 2.1%, respectively. In most cases, substantial economic benefits also can be
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achieved by GHG emission mitigation. An uncertainty analysis showed that enhancing
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the annual working time or serviceable lifetime levels could strengthen the GHG
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mitigation potential at a lower cost for all of the measures.
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1. INTRODUCTION
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As the world largest CO2 emitter, China has emphasized the mitigation of carbon
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emissions and has an objective of reaching peak CO2 emissions prior to 20301. Energy
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consumption is the dominant contributor to climate change, accounting for around 60%
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of global greenhouse gas (GHG) emissions.2 Therefore, reducing the carbon intensity
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of energy infrastructure is critical to achieving long-term climate goals. Coal
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consumption control and energy infrastructure optimization have been proposed as key
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measures for reducing emissions.3 Energy-related GHG emissions are correlated with
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energy infrastructure properties (e.g., capacity, technology, fuel type, and vintage).
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Because of its long service life, energy infrastructure will play a significant role in
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reducing CO2 emissions via the deployment of low-carbon retrofitting.
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This study draws upon the case of GHG mitigation in Chinese eco-industrial parks
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(EIP) and focuses on energy infrastructure stocks. This is the second article by our
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group researching this topic. In our previous work4, we defined energy infrastructure in
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eco-industrial parks as “a shareable energy utility that is located within the physical
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boundary of an industrial park and provides secondary energy for the park by
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converting primary energy into, for example, heat or electricity.” The energy
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infrastructure stocks under consideration are mainly divided into three categories:
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combined heat and power (CHP) plants, electricity-generating plants, and
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heat-generating plants. We conducted an in-depth analysis of the evolution of 548
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serviceable energy infrastructure units in 106 Chinese eco-industrial parks (including
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vintage years, fuel input diversity levels, energy outputs, and technologies). We
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measured the GHG emissions from the energy infrastructure stocks in the
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eco-industrial parks to illustrate the importance of the low-carbon development of such
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stocks. The energy infrastructure stocks in Chinese EIPs are heavily coal dependent,
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and coal-fired units account for 87.5% of the total capacity levels.4 Statistically, energy
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infrastructure stocks account for roughly 75.2% (median value) of the direct GHG
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emissions from these eco-industrial parks related to fuel consumption.4
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In-use stocks, which are primarily in the form of environmental and manufactured
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stocks, are essential characteristics of a system’s metabolism.5 Numerous studies have
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modeled in-use stocks, including stocks of natural resources, such as forests6, fish7,
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and manufactured stocks, such as buildings8, roads9, and vehicles10. The material flow
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analysis approach is now a particularly well-established methodology for quantifying
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stocks, flows, inputs, and losses for a given resource11, and it has been applied in
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dynamic analyses of more than 60 metals12. Busch et al. presented an enhanced stock
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and flow model to dynamically assess the material demands of infrastructure
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transitions.13
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In-use stocks also show links between services for human beings and
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energy/material consumption, suggesting that climate change mitigation requires the
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decoupling of these links.14 Energy infrastructure stocks are characterized by a long
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service lifetime thus giving the possibility of path-dependent carbon lock-in.15 At the
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same time, their low-carbon transition will likely be gradual as amid the multiple
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options for reducing GHG emissions.16 Davis et al. quantitatively assessed the
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relationship between climate change and cumulative CO2 emissions from existing
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primary energy capital stock and identified a significant emission inertia in China due
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to the expansion trend of the stock.17 Felgenhauer and Webster studied the interactions
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among climate response portfolios for mitigation, short-term flow adaptation and
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long-term stock adaptation and recommended directions for near-term policy
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decisions.18
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Energy infrastructure has grown greatly in China and some have claimed that
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there is some redundancy.19 The stocks in EIPs were mostly created after 19934. Based
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on a 30-year service lifetime20, the energy infrastructure stocks in Chinese EIPs will
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accumulate continuously and present a slow turnover rate. Therefore, it is necessary to 3
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determine the cost effectiveness of retrofitting such stocks. Modeling these stocks will
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provide a quantitative assessment of GHG mitigation strategies for industrial parks in
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China. Stock-based models can help facilitate insightful planning for the mitigation of
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climate issues. Hertwich et al. developed a life-cycle assessment (LCA) model that
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includes a foreground technology database, a background LCA database, multiregional
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input-output tables, and a vintage capital model for assessing the environmental
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impacts and energy and material requirements of fossil fuel- and renewable
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energy-based electricity supply technologies under exogenous scenario assumptions
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until 2050.21 A dynamic type-cohort-time approach is deployed by Vásquez et al to
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model the building stock to examine the effect of policies on reducing energy use and
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GHG emissions.22 Modaresi et al proposed a dynamic stock model coupling with a
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dynamic MFA to study the GHG emission savings of material substitution in
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passenger cars.23 Liu et al modeled the global aluminum cycle using a dynamic MFA
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to explore associated GHG emission pathways and mitigation potentials.24
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This article presents a vintage stock model to quantify the GHG mitigation
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potential and cost associated with five key measures identified in our previous work4.
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Our model focuses on the transformation of current in-use stocks according to key
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measures and bottom-up data. Based on the different vintages of energy infrastructure
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stocks, the model aims to examine the GHG mitigation potential and cost of each
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measure via unit-by-unit and vintage-wise accounting that targets the energy
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infrastructure stocks in the 106 eco-industrial parks examined under different scenarios.
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Specifically, a unit-by-unit approach is deployed to illustrate the GHG mitigation
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potential and cost across the serviceable lifetime of each energy infrastructure stock
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before and after employing appropriate measures individually or in a combined manner.
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Uncertainty within the model is quantitatively assessed and then policy implications
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are identified.
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2. MATERIALS AND METHODS
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2.1 Research objective and data collection
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Based on the above definition of energy infrastructure in industrial parks, fossil
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fuel-based energy infrastructure is the focus of our research objective. For
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conventional energy infrastructure stocks, such as coal-fired electricity-generating
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plants or CHP plants, the units include several boilers and one turbine25. In general, the
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capacity of a unit is represented by the scale of a turbine (for CHP and electricity
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generation) or boiler (for heat generation).
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Through a bottom-up investigation, we established an inventory of fossil
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fuel-fired energy infrastructure stocks in the 106 analyzed Chinese eco-industrial parks,
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which included 164 utilities (548 units). We collected the following information: (1)
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plant-level information, including fuel inputs and secondary energy outputs; and (2)
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unit-level information, including individual capacities, technology types, and vintages.
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Detailed information and data sources can be found in the Supporting Information (SI).
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2.2 Modeling
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A collection of measures was put into practice during the evolution of energy
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infrastructure stocks in Chinese EIPs. Five key GHG mitigation measures are proposed
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in our previous study. To be able to analyze these five, we propose a vintage stock
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model (see Figure 1) coupled with a unit-by-unit cost-effectiveness analysis under
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different scenarios to determine the potential for and cost of GHG emissions reduction
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measures for energy infrastructure in Chinese eco-industrial parks.
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Figure 1 Vintage stock model for GHG mitigation in Chinese industrial parks by
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targeting the energy infrastructure
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The model includes six components: (1) five key measures, (2) matching criteria 5
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of the key measures for appropriate candidates, (3) energy efficiency assessments of
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each unit, (4) GHG emissions accounting, (5) cost-effectiveness analysis, and (6)
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scenario setup. These six components and their connections are shown in Figure 1, and
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they are illustrated in detail below.
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As discussed in our previous work4, fuel-type shifting and energy-efficiency
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improvements are two central measures used to reduce GHG emissions from energy
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infrastructure, and our model is based on the five key GHG mitigation measures
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identified. These measures are defined as follows:
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(1) M1 refers to the transformation of coal-fired units into natural gas (NG)-fired units via retrofitting boilers;
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(2) M2 refers to the replacement of certain coal-fired units with municipal solid
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waste (MSW)-to-energy units through the use of newborn incinerators rather than
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coal-fired boilers;
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(3) M3 refers to the replacement of small-capacity coal-fired units with large-capacity coal-fired units; (4) M4 refers to the upgrading of extraction-condensing coal-fired units to back-pressure coal-fired units by retrofitting turbines; and (5) M5 refers to the replacement of coal-fired units with natural gas combined cycle (NGCC) units, achieving both fuel replacement efficiency improvements.
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M1 to M5 are basic GHG mitigation measures that may have the potential to be
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integrated in practice. M3 and M4 can be integrated with M1 or M2 accordingly (e.g.,
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‘M1 & M3’ involves coal consumption shifting to natural gas use along with the
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replacement of large-capacity units with small units, and ‘M1 & M4’ refers to the
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replacement of coal consumption with natural gas use along with the retrofitting of
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extraction-condensing turbines with back-pressure turbines). It is not reasonable to
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combine M3 and M4 because back-pressure turbines generally have a capacity of less
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than 200 MW and are not listed as large-capacity turbines (generally those with a
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capacity of 300 MW or larger26). The energy infrastructure shared among neighboring
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parks is also considered in M3 and M5.
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In the vintage-stock model, we do not include renewable and biomass energy as
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key measures for mitigating current GHG emissions in eco-industrial parks because
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energy infrastructure stocks in the EIPs are highly coal-dependent and coal-fired units
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account for 87.5% of the total capacity, which is much higher than the capacity for the
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entire country (60.7% in 201427). With this in mind, we focused on upgrading or
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transforming existing coal-fired energy infrastructure stocks in this study. Certain
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energy utilities use coal gangue (residue) or recover chemical reaction heat4; however,
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these utilities are limited to specific industrial parks, and they are also not listed as
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general GHG mitigation mechanisms. In M5, NGCC technologies are considered
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rather than integrated gasification combined cycle (IGCC) technologies because of the
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higher costs and lower levels of reliability associated with IGCC plants28. Thus, we
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narrowed down the stocks as fossil fuel-fired stocks and excluded those fueled by
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renewable energy biomass energy.
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The definitions of the parameters and variables of the vintage stock model are listed in Table 1 and applied in the following sections.
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Table 1. Main parameters and variables of the vintage stock model
Definition Number of units belonging to energy infrastructure i, e.g., i=1…164 Capacity of the jth unit in energy infrastructure i (MW), e.g., j=1... Vintage year (such as 1993) of the jth unit in energy infrastructure i Year during which the M1-M5 is applied, supposed to 2016 Remaining serviceable years of the jth unit in energy infrastructure i. Assuming a serviceable lifetime of 30 years20 for all units, then
= + 30 − .
Annual working hours of the energy infrastructure (hours per year) Fuel category of the jth unit of the energy infrastructure i, ∈ {coal, natural gas (NG), petrol, coal gangue, municipal solid waste (MSW), and sludge} ( ) GHG emissions per GJ of consumption (MtCO2e/GJ, see Table S1 in the Supporting Information) ℎ Technology of the jth unit of energy infrastructure i, ℎ ∈ {Extraction-condensing (EC), Back-pressure (BP), NGCC, etc.} (/ ) Heat-to-electric ratio of energy infrastructure i 7
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%&/%&∗
( %&
)*+),
- / .
%& 00
%&∗ 00
0045)*+), 455)*+), 45 )*+),
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Energy efficiency of energy infrastructure i and its follow-up units in the baseline scenario Boiler efficiency of energy infrastructure i in the baseline scenario Energy efficiency of the jth unit in energy infrastructure i for applicable scenarios of M1-M5 Fuel consumption of the jth unit in the energy infrastructure i in under baseline scenario (GJ/a), 29.27 GJ=1 tce GHG emission of the jth unit in energy infrastructure i for [ , + 30] in the baseline scenario (M t CO2e) GHG emission of the jth unit of energy infrastructure i and its follow-up unit for [ , + 30] in the baseline scenario (M t CO2e) GHG emission mitigation for the M1-M5 scenarios (M t CO2e) GHG emission mitigation rate for the M1-M5 scenarios Net cost of the GHG emission reduction for the M1-M5 scenarios (CNY/t CO2e) Ratio of coal to MSW for newly established MSW incinerators in the M2 scenario
2.2.1 Energy efficiency assessment of energy infrastructure
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Energy outputs of the energy infrastructure in an EIP include electricity and
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thermal energy in most cases. Steam is the most popular thermal output (only one EIP
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energy infrastructure generates both steam and hot water), and the energy efficiency of
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an energy infrastructure can be formulated according to equation 1:
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-67 88 - 7 ( ) = IA;: JF> 9:; &=;DE =F G>F M2 > M4 > M3.
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Based on the cost for GHG mitigation, the sequence is as follows: M2 < M2&M4
