- Email: [email protected]
Optimal Carbon Reduction Strategies in the Building Sector with Emission Trading System
Available online at www.sciencedirect.com
ScienceDirect Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2017) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia
Energy (2017) 000–000 307–312 EnergyProcedia Procedia143 00 (2017) www.elsevier.com/locate/procedia
World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference, WES-CUE 2017, 19–21 July 2017, Singapore
Optimal The Carbon Reduction Strategies in theHeating Building Sector with 15th International Symposium on District and Cooling Emission Trading System
Assessing the feasibility of using the heat demand-outdoor c Xiangnan for Songaa,c* , Yujie Lub, district Chenyangheat Shuaidemand temperature function long-term forecast aa Research Assistant. Department of Building, School of Design and Environment, National University of Singapore, Singapore 117566 a,b,c a a b c c PhD and Assistant Professor. Department of Building, School of Design and Environment, National University of Singapore, Singapore 117566 cc PhD student. School of Construction Management and Real Estate, Chongqing University, Chongqing 400045, China a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract bb PhD
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
Building sector is commonly appreciated as a major contributor to the global warming. Hence, the control of energy consumption and carbon emissions from buildings has received unprecedented attention and concern by Chinese government and practitioners. Abstract This study is to present an optimization model to quantitatively assess carbon reduction strategies at enterprise level in building sector under the multi-objective: to achieve regulated carbon emissions reduction target set by government, as well as to realize District heating networks costs are commonly in the literature as one of the mostcarbon effective solutions for decreasing the the minimum incremental incurred byaddressed carbon emissions. The available alternative emissions reduction strategies greenhouseingas from the building These systems require high investments which are returned heat considered thisemissions paper comprise adopting or sector. upgrading low-carbon technologies, participating Emission Tradingthrough System the (ETS) sales. Due to the changed and building regulations. renovation policies, heat demand inputs the forward future could decrease, market, and releasing carbon climate dioxide conditions violating environmental This work innovatively mathematical prolonging the investment return period. formulas to explore the impact of government environmental inspection on violations and accompanying loss of reputation. A The main scopehotel of this is to assess the feasibility of using theinvestigate heat demand outdoor temperature for heat demand typical four-star inpaper Shenzhen is selected as an actual case to the–proposed model basedfunction on empirical analysis. forecast. district of amount Alvalade, located in Lisbon (Portugal), washighest used as a case tCO study. The district is consisted of 665 The result The shows that the of non-complying emissions are the of 2388.4 22/year, followed by LCT-mitigated buildings are that2112.3 vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district emissions tCO 22/year, and yet emissions permits from ETS market are zero. The findings of this study can facilitate renovation scenarios were developed (shallow, To estimate error, obtained heat demand were building owners formulate more reasonable andintermediate, cost-effectivedeep). strategies option the to achieve the regulated carbon values emissions compared with results from a dynamic heat demand model, previously developed and validated by the authors. reduction target. results showed that when only weatherLtd. change is considered, the margin of error could be acceptable for some applications ©The 2017 The Authors. Published by Elsevier ©(the 2017 TheinAuthors. Published by Ltd.20% error annual demand was lower than for all weather scenarios considered). introducing renovation Peer-review under responsibility of Elsevier the scientific committee of the World Engineers SummitHowever, – Appliedafter Energy Symposium & Peer-review under responsibility of theupscientific committee of the World Engineers Summit – Applied Energy Symposium & scenarios, the error value increased to 59.5% (depending on the weather and renovation scenarios combination considered). Forum: Low Carbon Cities & Urban Energy Joint Conference. Forum: Lowof Carbon & Urban Energyon Joint Conference. The value slope Cities coefficient increased average within the range of 3.8% up to 8% per decade, that corresponds to the decrease Carbon in the reduction number of heating hours of 22-139h thetrading heating season (depending on technologies; the combination of weather and Keywords: Strategy; Decision-support model;during Emission system (ETS); Low-carbon Non-compliance. renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. **Corresponding author. Tel: +65 84326365. E-mail address: [email protected]
Keywords: Heat demand; Forecast; Climate change
1876-6102 © 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the scientific committee of the World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference. 10.1016/j.egypro.2017.12.689
308 2
Xiangnan Song et al. / Energy Procedia 143 (2017) 307–312 Author name / Energy Procedia 00 (2017) 000–000
1. Introduction China has witnessed a big increase in carbon emissions for the last two decades, recording 8.5 Giga ton CO 2 in 2012 with responsible for nearly three-quarters of global carbon emissions (Liu et al., 2015). Carbon emissions generated from the building sector is commonly appreciated as a major contributor (Chau et al., 2015), which is driven by the country’s large population, unprecedented development of urbanization, and continuously increasing demand in energy service (Shuai et al., 2017). With increasing concern for carbon emissions mitigation, the emphasis of the low carbon transition policies in China has gradually shifted from mandatory regulations to market-based mechanisms in recent years (Shen et al., 2016). ETS allows building owners flexibly trade their excess or saved carbon emissions from the trading market. After the introduction of ETS, building owners who release excess carbon dioxide would have three carbon mitigation options to meet the required carbon reduction goals−adopting low-carbon technologies, purchasing carbon emission permits from ETS market, and irresponsibly releasing emissions at the risk of being punished. Selection any of above option will impose an additional burden on building owner’s expense. As a response, building owners will opt to a rational decision to minimize the whole losses due to emission reduction. Therefore, it is challenge for building owners to decide emissions strategies with minimal interruption to building operation and management. To bridge this gap, this paper aims to propose a multi-objective optimization model that helps building owners quantitatively assesses carbon reduction strategies to achieve emissions reduction target imposed by government with minimum incremental costs. Through this study, a comprehensive and reliable recommendation about carbon emissions reduction strategies option can be achieved in order to help owners formulate more appropriate decisions with external factors change. 2. Optimization model setup 2.1. Assumptions In practice, most of energy saving and emissions reduction measures in building sector involve certain behavioral and management issues, such as opening windows, turning off lights timely, adjusting air-conditioning temperature et al. Compared with the aforesaid three strategies, these management and behavior measures would not impose additional cost. So, if one building owner confronts a mandatory carbon emission target, these measures will be preferred. As for the decision-making problem, a major concern for building owners is to select and decide appropriate strategies among those actions imposed additional costs. So behavioral and management measures are outside the research scope of this paper. The scope of this study is limited to those public buildings released excess carbon emissions during the operation phrase with respect to the baseline value that is regulated by the government. Building owners in this research refer to those who are responsible for mitigating carbon emissions. When the building is rented, the responsible actor is a renter. Our initial research hypothesis was that if the enterprise release carbon emissions violating the environmental regulations and government just detect it, the issue of violation and the exact volume will be sure caught and punished by government. Second, building owners are aware of the energy consumption performance and the marginal abatement cost of their own buildings. 2.2. Costs of different strategies The carbon emissions trading costs that are borne by building owners refer to costs for purchasing 1) carbon emissions permits, 2) transaction commission fee that is charged proportionately to the costs for purchasing emissions permit by the Emission Exchange, and 3) other related costs that include MRV costs, membership fee charged by Emission Exchange, staff training expense for trading practices, administrative costs, and so on. Accordingly, CET costs can be calculated with the mathematical formula as follows:
Xiangnan Song et al. / Energy Procedia 143 (2017) 307–312 Author name / Energy Procedia 00 (2017) 000–000
309 3
ETS if qi 0 pqiETS pqiETS ， (1) TC ( qi )= ETS if qi 0 0， ETS
ETS
ETS
Where TC ( qi ) refers to the total carbon emissions trading costs, qi
refers to the quantity of carbon emissions
permits purchased by building i in ETS market, p refers to the carbon price, refers to the commission rate, refers to fixed costs including MRV costs, membership fee, training fee, administrative costs and so on. The LCT costs for a building refer to upfront capital invested in LCT, such as upgrading heating system, lighting system, thermal solar system, household appliance, air-conditioning system and so on. TEC
In this study, the total LCT costs IC ( qi
) can be calculated through the integration of MAC for individual
building system, which is depicted as follows: TEC
qi TEC
IC ( qi
MAC (q
)
TEC
TEC
) dqi
i
(2)
0
TEC
TEC
Where MAC ( qi ) refers to the marginal abatement costs; qi
is the amount of carbon reductions through
adopting carbon mitigation technologies. In this study, a linear relationship is adopted based on the recommendation by Lee and Han (2016) as the most appropriate model at enterprise level. TEC
TEC
MAC ( qi ) ai qi
(3)
bi
TEC
TEC
Where MAC ( qi ) is the marginal abatement cost with regard to achieving carbon reductions qi
TEC
; qi
is the
amount of carbon reductions through adopting LCT; a i and bi are parameters to be estimated. The MAC of the building can be calculated as follows: m
AC TEC
MAC ( qi
)
TEC j i
TEC j
qi
j 1
m
q
(4) TEC j i
j 1
q TEC m q TEC i i j 1 Subject to q TEC R TEC i i j
Where ACi
TEC j
TEC j
building i; qi
j
(5)
j
refers to the average investment costs in unit floor area regarding the technology j adopted in TEC j
refers to the carbon reduction quantity through technology j adopted in building i; Ri
is the
maximum potential carbon emissions reduction quantity of technology j adopted in building i; m is the total amount of technologies adopted in building i. TEC
Accordingly, the total investment cost IC ( qi
) can be calculated by the following formula: TEC
qi TEC
IC ( qi
)=
(a q i
0
TEC
i
TEC
bi ) dqi
(6)
The amount of financial penalties incurred by violations are proportionate to the carbon price in the ETS market according to the current environmental regulations and carbon emissions trading rules (Beijing Municipal People's Government, 2014). As the penalties would only be executed by the government when the non-complying building owner is caught, the relationship between the probability of government environmental inspection and associated pollutant emissions should be considered when estimating the penalties. As a result, the function of penalties for non-compliance can be expressed as follows:
Xiangnan et al.Procedia / Energy00 Procedia 143 (2017) 307–312 Author nameSong / Energy (2017) 000–000
310 4
PC ( qi ) ( qi ) pqi VIO
VIO
Where qi
VIO
VIO
(7)
is the quantity of non-complying emissions, ( qi ) refers the probability of government VIO
environmental inspection, is the times of non-complying penalty to the average carbon price set by the related environmental regulations; p is the average carbon price in the ETS market. The Logistic Model is employed on the basis of many previous studies to estimate the probability of inspection function in this paper, which is shown as follows: 1 VIO ( qi ) (8) s t log ( q ) 1 e Then, the loss of reputation incurred by non-compliance will be assessed. As the degree of reputation loss and financial penalties are all proportionate to the quantity of non-complying carbon emissions, the authors attempt to reference the penalties to estimate its accompanying loss of reputation in this paper. And it is assumed that the correlation coefficient between the loss of reputation and the non-complying financial penalty is , which can be estimated by the ratio of agent’s market value dropped after and before the environment incident to the dropped value after and before the financial penalty. Accordingly, the loss of reputation can be depicted as follows: 1 VIO VIO VIO RC (qi ) PC (qi )= ( pqi ) (9) a b log ( q ) 1 e So, the non-compliance costs can be obtained: 1 VIO VIO VIO NC ( qi ) ( pqi pqi ) （10） a b log ( q ) 1 e The main goal of this study is to find optimal strategies that help building owners achieving the emissions reduction target with minimal incremental cost. So the multi-objective model is shown as follows: VIO
10
i
VIO
10
i
VIO
10
i
q 1 ETS TEC TEC MinC ( q ) Min (1 ) pqi + ( ai qi bi ) dqi a b log 1 e 0 ETS TEC VIO qi +qi qi =q ETS qi 0 (11) Subject to TEC qi 0 qVIO 0 i TEC
i
(1 ) pqi
VIO
VIO
( qi 10
)
As the MATLAB function ‘fmincon’ is a typical technique for solving this type of problem. Therefore, MATLAB 2012b is employed in this research and the source code is available upon request. The empirical case to illustrate the optimization is shown in the following Section. 3. China’s building sector: an empirical study The ETS pilot in Shenzhen is the pioneer incorporating the building sector to the sectoral coverage among seven pilots in China and therefore selected as reference in this study. In this study, a typical franchise four-star hotel has been selected as an actual case. The hotel is located in Nanshan District and has operated since 1999, with total 33 floors, 141 meters of height, and 60,010 square meters of floor area. After more than 15 years of operation, all building systems, such as facades, air-conditionings, and lighting are deteriorated and demand deep energy retrofit and carbon emissions reduction. Currently, its energy consumption is as high as 203.5 kWh/m 2/year which is far beyond the government baseline of 120 kWh/m2/year. According to the trading rules in Shenzhen ETS pilot, the retrofit report with respect to the case building, environmental inspection database released by Human Settlements and Environment Commission of Shenzhen
Xiangnan Song et al. / Energy Procedia 143 (2017) 307–312 Author name / Energy Procedia 00 (2017) 000–000
311 5
Municipality etc.(Housing and Construction Bureau of Shenzhen Municipality, 2016; Human Settlements and Environment Commission of Shenzhen Municipality, 2016a, b, 2016, 2017), the parameters of the proposed optimization model in Section 2 can be estimated, which are summarized in Table 1. Table 1 Summary of parameters in the proposed optimization model Symbol
Unit
Estimated Value
q
tCO2
4500.75
p
CNY/ tCO2
26
-
6‰
CNY
18,000
ai , bi
-
0.01122, 0.5232
s ,t
-
19.89, 5.13
-
3
Ai ei e0 i
-
0.35
m2
60,010
tCO2/m2/year
0.193
tCO2/m2/year
0.118
We solved the above objective function by using MATLAB and obtained the results. The q
VIO i
,q
TEC i
,q
ETS i
are 2388.4
tCO2, 2112.3 tCO2, and 0 tCO2 respectively. Specifically, the most cost-effective carbon emissions reduction strategy for the selected four-star hotel is that: the amount of carbon emissions reduction through LCT is 2112.3 tCO2/year, the amount of non-complying emissions is 2388.4 tCO2/year, and yet emissions permits from ETS market is zero. The result is consistent with previous studies, which showed that no practical carbon trading of building projects had been conducted at present (Wang et al., 2017). The result of the case indicates that the currently being vigorously promoted ETS has not been viewed as a priority by this owner, but the traditional strategies of releasing carbon dioxide violating environmental regulations and adopting LCT are still favored at present. 4. Discussion Followed by this strategy, the hotel owner will spend CNY 41,968 to achieve the regulated emissions reduction target with CNY18, 042 on violations and CNY 23,926 on LCT respectively. The significant difference of carbon reductions among the three strategies is mainly due to the low marginal abatement cost in LCT and non-compliance but high in ETS. It can be calculated according to formulas (4.2) and (4.6) that the MAC of LCT is CNY 23.176/tCO2, non-compliance is CNY 23.19 t/CO2, and ETS is more than CNY 26 t/CO 2. As can be seen, there is still potential of carbon emissions reduction in LCT for the selected hotel. For instance, the total amount of carbon reduction through current four low-carbon technologies in planning is just 1632 tCO2/year with MAC at CNY 18.51 t/CO2, and there exists 480 tCO2/year to be mitigated through other LCTs in different building systems such as exterior wall, water heating, and house-appliance. The result of the case indicates that the currently being vigorously promoted ETS has not been viewed as a priority by this owner, but the traditional strategies of releasing carbon dioxide violating environmental regulations and adopting LCT are still favoured at present. 5. Conclusion This study incorporates the ETS into building owners’ carbon reduction strategies when they confronted regulated carbon emissions constraints. Firstly, an optimization model was proposed to quantitatively assess carbon reduction strategies at enterprise level in the building sector under the multi-objective: to achieve regulated carbon emissions reduction target set by government, as well as to realize the minimum incremental costs incurred by
312 6
Xiangnan Song et al. / Energy Procedia 143 (2017) 307–312 Author name / Energy Procedia 00 (2017) 000–000
carbon emissions reduction. Then, a typical four-star hotel in Shenzhen was selected as an actual case to investigate the strategy option based on empirical analysis. It was found that the ETS in building sector is often in a state of market failure, and violation is favored in most cases. This study has two prominent contributions to the existing knowledge. First, the decision-support model proposed in this paper provide a new perspective with integrating ETS and violation with available alternative strategies at the enterprise level in building sector, which makes carbon reduction strategy decision more scientific and comprehensive. Second, it innovatively investigates the impact of government environmental inspection on violations and accompanying loss of reputation by putting forward mathematical formulas to explore the quantitative relationship among them, which enables the estimation more accuracy and persuasive. It is appreciated that several limitations identified need to be further studied in the future. For instance, the case study only explores the type of four-star hotel in Shenzhen. It is suggested that future studies continue to extend the proposed decision-support model in other types of buildings, such as office buildings, commercial buildings in more cities and regions. Additionally, due to lack of enough reliable practical data in violations and ETS market in building sector, some data for estimating parameters in establishing model are borrowed from other relevant fields, which may bring some uncertainty to the result. So, with the continuous promotion of ETS in building sector, more targeted data and information can be collected to improve the accuracy of the proposed model in this paper. References [1] Beijing Municipal People's Government, 2014. Temporary Management Methods for the Carbon Emissions Trading in Beijing, retrived from http://www.bjets.com.cn/article/zcfg/201407/20140700000255.shtml. [2] Chau, C., Leung, T., Ng, W., 2015. A review on life cycle assessment, life cycle energy assessment and life cycle carbon emissions assessment on buildings. Applied Energy 143, 395-413. [3] Housing and Construction Bureau of Shenzhen Municipality, 2016. The benefits and risk assessment report for building carbon emissions trading in Shenzhen, China. [4] Human Settlements and Environment Commission of Shenzhen Municipality, 2016a. The implementation plan for regulatory inspection about pollution sources in Shenzhen, retrived from http://www.szhec.gov.cn/xxgk/ywgz/hjjc/201605/t20160529_3665543.htm. [5] Human Settlements and Environment Commission of Shenzhen Municipality, 2016b. The information disclosure column of regulatory about Shenzhen's key pollution sources. [6] Human Settlements and Environment Commission of Shenzhen Municipality, 2016, 2017. The publicity of government regulatory environmental inspection results of Shenzhen, retrived from http://www.szhec.gov.cn/xsdw/hjjczd/bmpt/. [7] Lee, K., Han, T.-W., 2016. How vulnerable is the emissions market to transaction costs?: An ABMS Approach. Energy Policy 90, 273-286. [8] Liu, Z., Guan, D., Wei, W., Davis, S.J., Ciais, P., Bai, J., Peng, S., Zhang, Q., Hubacek, K., Marland, G., Andres, R.J., Crawford-Brown, D., Lin, J., Zhao, H., Hong, C., Boden, T.A., Feng, K., Peters, G.P., Xi, F., Liu, J., Li, Y., Zhao, Y., Zeng, N., He, K., 2015. Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature 524, 335-338. [9] Shen, L., Song, X., Wu, Y., Liao, S., Zhang, X., 2016. Interpretive Structural Modeling based factor analysis on the implementation of Emission Trading System in the Chinese building sector. Journal of Cleaner Production 127, 214-227. [10] Shuai, C., Shen, L., Jiao, L., Wu, Y., Tan, Y., 2017. Identifying key impact factors on carbon emission: Evidences from panel and time-series data of 125 countries from 1990 to 2011. Applied Energy 187, 310-325. [11] Wang, Z., Zhao, J., Li, M., 2017. Analysis and optimization of carbon trading mechanism for renewable energy application in buildings. Renewable and Sustainable Energy Reviews 73, 435-451.
ScienceDirect Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2017) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia
Energy (2017) 000–000 307–312 EnergyProcedia Procedia143 00 (2017) www.elsevier.com/locate/procedia
World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference, WES-CUE 2017, 19–21 July 2017, Singapore
Optimal The Carbon Reduction Strategies in theHeating Building Sector with 15th International Symposium on District and Cooling Emission Trading System
Assessing the feasibility of using the heat demand-outdoor c Xiangnan for Songaa,c* , Yujie Lub, district Chenyangheat Shuaidemand temperature function long-term forecast aa Research Assistant. Department of Building, School of Design and Environment, National University of Singapore, Singapore 117566 a,b,c a a b c c PhD and Assistant Professor. Department of Building, School of Design and Environment, National University of Singapore, Singapore 117566 cc PhD student. School of Construction Management and Real Estate, Chongqing University, Chongqing 400045, China a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract bb PhD
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
Building sector is commonly appreciated as a major contributor to the global warming. Hence, the control of energy consumption and carbon emissions from buildings has received unprecedented attention and concern by Chinese government and practitioners. Abstract This study is to present an optimization model to quantitatively assess carbon reduction strategies at enterprise level in building sector under the multi-objective: to achieve regulated carbon emissions reduction target set by government, as well as to realize District heating networks costs are commonly in the literature as one of the mostcarbon effective solutions for decreasing the the minimum incremental incurred byaddressed carbon emissions. The available alternative emissions reduction strategies greenhouseingas from the building These systems require high investments which are returned heat considered thisemissions paper comprise adopting or sector. upgrading low-carbon technologies, participating Emission Tradingthrough System the (ETS) sales. Due to the changed and building regulations. renovation policies, heat demand inputs the forward future could decrease, market, and releasing carbon climate dioxide conditions violating environmental This work innovatively mathematical prolonging the investment return period. formulas to explore the impact of government environmental inspection on violations and accompanying loss of reputation. A The main scopehotel of this is to assess the feasibility of using theinvestigate heat demand outdoor temperature for heat demand typical four-star inpaper Shenzhen is selected as an actual case to the–proposed model basedfunction on empirical analysis. forecast. district of amount Alvalade, located in Lisbon (Portugal), washighest used as a case tCO study. The district is consisted of 665 The result The shows that the of non-complying emissions are the of 2388.4 22/year, followed by LCT-mitigated buildings are that2112.3 vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district emissions tCO 22/year, and yet emissions permits from ETS market are zero. The findings of this study can facilitate renovation scenarios were developed (shallow, To estimate error, obtained heat demand were building owners formulate more reasonable andintermediate, cost-effectivedeep). strategies option the to achieve the regulated carbon values emissions compared with results from a dynamic heat demand model, previously developed and validated by the authors. reduction target. results showed that when only weatherLtd. change is considered, the margin of error could be acceptable for some applications ©The 2017 The Authors. Published by Elsevier ©(the 2017 TheinAuthors. Published by Ltd.20% error annual demand was lower than for all weather scenarios considered). introducing renovation Peer-review under responsibility of Elsevier the scientific committee of the World Engineers SummitHowever, – Appliedafter Energy Symposium & Peer-review under responsibility of theupscientific committee of the World Engineers Summit – Applied Energy Symposium & scenarios, the error value increased to 59.5% (depending on the weather and renovation scenarios combination considered). Forum: Low Carbon Cities & Urban Energy Joint Conference. Forum: Lowof Carbon & Urban Energyon Joint Conference. The value slope Cities coefficient increased average within the range of 3.8% up to 8% per decade, that corresponds to the decrease Carbon in the reduction number of heating hours of 22-139h thetrading heating season (depending on technologies; the combination of weather and Keywords: Strategy; Decision-support model;during Emission system (ETS); Low-carbon Non-compliance. renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. **Corresponding author. Tel: +65 84326365. E-mail address: [email protected]
Keywords: Heat demand; Forecast; Climate change
1876-6102 © 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the scientific committee of the World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference. 10.1016/j.egypro.2017.12.689
308 2
Xiangnan Song et al. / Energy Procedia 143 (2017) 307–312 Author name / Energy Procedia 00 (2017) 000–000
1. Introduction China has witnessed a big increase in carbon emissions for the last two decades, recording 8.5 Giga ton CO 2 in 2012 with responsible for nearly three-quarters of global carbon emissions (Liu et al., 2015). Carbon emissions generated from the building sector is commonly appreciated as a major contributor (Chau et al., 2015), which is driven by the country’s large population, unprecedented development of urbanization, and continuously increasing demand in energy service (Shuai et al., 2017). With increasing concern for carbon emissions mitigation, the emphasis of the low carbon transition policies in China has gradually shifted from mandatory regulations to market-based mechanisms in recent years (Shen et al., 2016). ETS allows building owners flexibly trade their excess or saved carbon emissions from the trading market. After the introduction of ETS, building owners who release excess carbon dioxide would have three carbon mitigation options to meet the required carbon reduction goals−adopting low-carbon technologies, purchasing carbon emission permits from ETS market, and irresponsibly releasing emissions at the risk of being punished. Selection any of above option will impose an additional burden on building owner’s expense. As a response, building owners will opt to a rational decision to minimize the whole losses due to emission reduction. Therefore, it is challenge for building owners to decide emissions strategies with minimal interruption to building operation and management. To bridge this gap, this paper aims to propose a multi-objective optimization model that helps building owners quantitatively assesses carbon reduction strategies to achieve emissions reduction target imposed by government with minimum incremental costs. Through this study, a comprehensive and reliable recommendation about carbon emissions reduction strategies option can be achieved in order to help owners formulate more appropriate decisions with external factors change. 2. Optimization model setup 2.1. Assumptions In practice, most of energy saving and emissions reduction measures in building sector involve certain behavioral and management issues, such as opening windows, turning off lights timely, adjusting air-conditioning temperature et al. Compared with the aforesaid three strategies, these management and behavior measures would not impose additional cost. So, if one building owner confronts a mandatory carbon emission target, these measures will be preferred. As for the decision-making problem, a major concern for building owners is to select and decide appropriate strategies among those actions imposed additional costs. So behavioral and management measures are outside the research scope of this paper. The scope of this study is limited to those public buildings released excess carbon emissions during the operation phrase with respect to the baseline value that is regulated by the government. Building owners in this research refer to those who are responsible for mitigating carbon emissions. When the building is rented, the responsible actor is a renter. Our initial research hypothesis was that if the enterprise release carbon emissions violating the environmental regulations and government just detect it, the issue of violation and the exact volume will be sure caught and punished by government. Second, building owners are aware of the energy consumption performance and the marginal abatement cost of their own buildings. 2.2. Costs of different strategies The carbon emissions trading costs that are borne by building owners refer to costs for purchasing 1) carbon emissions permits, 2) transaction commission fee that is charged proportionately to the costs for purchasing emissions permit by the Emission Exchange, and 3) other related costs that include MRV costs, membership fee charged by Emission Exchange, staff training expense for trading practices, administrative costs, and so on. Accordingly, CET costs can be calculated with the mathematical formula as follows:
Xiangnan Song et al. / Energy Procedia 143 (2017) 307–312 Author name / Energy Procedia 00 (2017) 000–000
309 3
ETS if qi 0 pqiETS pqiETS ， (1) TC ( qi )= ETS if qi 0 0， ETS
ETS
ETS
Where TC ( qi ) refers to the total carbon emissions trading costs, qi
refers to the quantity of carbon emissions
permits purchased by building i in ETS market, p refers to the carbon price, refers to the commission rate, refers to fixed costs including MRV costs, membership fee, training fee, administrative costs and so on. The LCT costs for a building refer to upfront capital invested in LCT, such as upgrading heating system, lighting system, thermal solar system, household appliance, air-conditioning system and so on. TEC
In this study, the total LCT costs IC ( qi
) can be calculated through the integration of MAC for individual
building system, which is depicted as follows: TEC
qi TEC
IC ( qi
MAC (q
)
TEC
TEC
) dqi
i
(2)
0
TEC
TEC
Where MAC ( qi ) refers to the marginal abatement costs; qi
is the amount of carbon reductions through
adopting carbon mitigation technologies. In this study, a linear relationship is adopted based on the recommendation by Lee and Han (2016) as the most appropriate model at enterprise level. TEC
TEC
MAC ( qi ) ai qi
(3)
bi
TEC
TEC
Where MAC ( qi ) is the marginal abatement cost with regard to achieving carbon reductions qi
TEC
; qi
is the
amount of carbon reductions through adopting LCT; a i and bi are parameters to be estimated. The MAC of the building can be calculated as follows: m
AC TEC
MAC ( qi
)
TEC j i
TEC j
qi
j 1
m
q
(4) TEC j i
j 1
q TEC m q TEC i i j 1 Subject to q TEC R TEC i i j
Where ACi
TEC j
TEC j
building i; qi
j
(5)
j
refers to the average investment costs in unit floor area regarding the technology j adopted in TEC j
refers to the carbon reduction quantity through technology j adopted in building i; Ri
is the
maximum potential carbon emissions reduction quantity of technology j adopted in building i; m is the total amount of technologies adopted in building i. TEC
Accordingly, the total investment cost IC ( qi
) can be calculated by the following formula: TEC
qi TEC
IC ( qi
)=
(a q i
0
TEC
i
TEC
bi ) dqi
(6)
The amount of financial penalties incurred by violations are proportionate to the carbon price in the ETS market according to the current environmental regulations and carbon emissions trading rules (Beijing Municipal People's Government, 2014). As the penalties would only be executed by the government when the non-complying building owner is caught, the relationship between the probability of government environmental inspection and associated pollutant emissions should be considered when estimating the penalties. As a result, the function of penalties for non-compliance can be expressed as follows:
Xiangnan et al.Procedia / Energy00 Procedia 143 (2017) 307–312 Author nameSong / Energy (2017) 000–000
310 4
PC ( qi ) ( qi ) pqi VIO
VIO
Where qi
VIO
VIO
(7)
is the quantity of non-complying emissions, ( qi ) refers the probability of government VIO
environmental inspection, is the times of non-complying penalty to the average carbon price set by the related environmental regulations; p is the average carbon price in the ETS market. The Logistic Model is employed on the basis of many previous studies to estimate the probability of inspection function in this paper, which is shown as follows: 1 VIO ( qi ) (8) s t log ( q ) 1 e Then, the loss of reputation incurred by non-compliance will be assessed. As the degree of reputation loss and financial penalties are all proportionate to the quantity of non-complying carbon emissions, the authors attempt to reference the penalties to estimate its accompanying loss of reputation in this paper. And it is assumed that the correlation coefficient between the loss of reputation and the non-complying financial penalty is , which can be estimated by the ratio of agent’s market value dropped after and before the environment incident to the dropped value after and before the financial penalty. Accordingly, the loss of reputation can be depicted as follows: 1 VIO VIO VIO RC (qi ) PC (qi )= ( pqi ) (9) a b log ( q ) 1 e So, the non-compliance costs can be obtained: 1 VIO VIO VIO NC ( qi ) ( pqi pqi ) （10） a b log ( q ) 1 e The main goal of this study is to find optimal strategies that help building owners achieving the emissions reduction target with minimal incremental cost. So the multi-objective model is shown as follows: VIO
10
i
VIO
10
i
VIO
10
i
q 1 ETS TEC TEC MinC ( q ) Min (1 ) pqi + ( ai qi bi ) dqi a b log 1 e 0 ETS TEC VIO qi +qi qi =q ETS qi 0 (11) Subject to TEC qi 0 qVIO 0 i TEC
i
(1 ) pqi
VIO
VIO
( qi 10
)
As the MATLAB function ‘fmincon’ is a typical technique for solving this type of problem. Therefore, MATLAB 2012b is employed in this research and the source code is available upon request. The empirical case to illustrate the optimization is shown in the following Section. 3. China’s building sector: an empirical study The ETS pilot in Shenzhen is the pioneer incorporating the building sector to the sectoral coverage among seven pilots in China and therefore selected as reference in this study. In this study, a typical franchise four-star hotel has been selected as an actual case. The hotel is located in Nanshan District and has operated since 1999, with total 33 floors, 141 meters of height, and 60,010 square meters of floor area. After more than 15 years of operation, all building systems, such as facades, air-conditionings, and lighting are deteriorated and demand deep energy retrofit and carbon emissions reduction. Currently, its energy consumption is as high as 203.5 kWh/m 2/year which is far beyond the government baseline of 120 kWh/m2/year. According to the trading rules in Shenzhen ETS pilot, the retrofit report with respect to the case building, environmental inspection database released by Human Settlements and Environment Commission of Shenzhen
Xiangnan Song et al. / Energy Procedia 143 (2017) 307–312 Author name / Energy Procedia 00 (2017) 000–000
311 5
Municipality etc.(Housing and Construction Bureau of Shenzhen Municipality, 2016; Human Settlements and Environment Commission of Shenzhen Municipality, 2016a, b, 2016, 2017), the parameters of the proposed optimization model in Section 2 can be estimated, which are summarized in Table 1. Table 1 Summary of parameters in the proposed optimization model Symbol
Unit
Estimated Value
q
tCO2
4500.75
p
CNY/ tCO2
26
-
6‰
CNY
18,000
ai , bi
-
0.01122, 0.5232
s ,t
-
19.89, 5.13
-
3
Ai ei e0 i
-
0.35
m2
60,010
tCO2/m2/year
0.193
tCO2/m2/year
0.118
We solved the above objective function by using MATLAB and obtained the results. The q
VIO i
,q
TEC i
,q
ETS i
are 2388.4
tCO2, 2112.3 tCO2, and 0 tCO2 respectively. Specifically, the most cost-effective carbon emissions reduction strategy for the selected four-star hotel is that: the amount of carbon emissions reduction through LCT is 2112.3 tCO2/year, the amount of non-complying emissions is 2388.4 tCO2/year, and yet emissions permits from ETS market is zero. The result is consistent with previous studies, which showed that no practical carbon trading of building projects had been conducted at present (Wang et al., 2017). The result of the case indicates that the currently being vigorously promoted ETS has not been viewed as a priority by this owner, but the traditional strategies of releasing carbon dioxide violating environmental regulations and adopting LCT are still favored at present. 4. Discussion Followed by this strategy, the hotel owner will spend CNY 41,968 to achieve the regulated emissions reduction target with CNY18, 042 on violations and CNY 23,926 on LCT respectively. The significant difference of carbon reductions among the three strategies is mainly due to the low marginal abatement cost in LCT and non-compliance but high in ETS. It can be calculated according to formulas (4.2) and (4.6) that the MAC of LCT is CNY 23.176/tCO2, non-compliance is CNY 23.19 t/CO2, and ETS is more than CNY 26 t/CO 2. As can be seen, there is still potential of carbon emissions reduction in LCT for the selected hotel. For instance, the total amount of carbon reduction through current four low-carbon technologies in planning is just 1632 tCO2/year with MAC at CNY 18.51 t/CO2, and there exists 480 tCO2/year to be mitigated through other LCTs in different building systems such as exterior wall, water heating, and house-appliance. The result of the case indicates that the currently being vigorously promoted ETS has not been viewed as a priority by this owner, but the traditional strategies of releasing carbon dioxide violating environmental regulations and adopting LCT are still favoured at present. 5. Conclusion This study incorporates the ETS into building owners’ carbon reduction strategies when they confronted regulated carbon emissions constraints. Firstly, an optimization model was proposed to quantitatively assess carbon reduction strategies at enterprise level in the building sector under the multi-objective: to achieve regulated carbon emissions reduction target set by government, as well as to realize the minimum incremental costs incurred by
312 6
Xiangnan Song et al. / Energy Procedia 143 (2017) 307–312 Author name / Energy Procedia 00 (2017) 000–000
carbon emissions reduction. Then, a typical four-star hotel in Shenzhen was selected as an actual case to investigate the strategy option based on empirical analysis. It was found that the ETS in building sector is often in a state of market failure, and violation is favored in most cases. This study has two prominent contributions to the existing knowledge. First, the decision-support model proposed in this paper provide a new perspective with integrating ETS and violation with available alternative strategies at the enterprise level in building sector, which makes carbon reduction strategy decision more scientific and comprehensive. Second, it innovatively investigates the impact of government environmental inspection on violations and accompanying loss of reputation by putting forward mathematical formulas to explore the quantitative relationship among them, which enables the estimation more accuracy and persuasive. It is appreciated that several limitations identified need to be further studied in the future. For instance, the case study only explores the type of four-star hotel in Shenzhen. It is suggested that future studies continue to extend the proposed decision-support model in other types of buildings, such as office buildings, commercial buildings in more cities and regions. Additionally, due to lack of enough reliable practical data in violations and ETS market in building sector, some data for estimating parameters in establishing model are borrowed from other relevant fields, which may bring some uncertainty to the result. So, with the continuous promotion of ETS in building sector, more targeted data and information can be collected to improve the accuracy of the proposed model in this paper. References [1] Beijing Municipal People's Government, 2014. Temporary Management Methods for the Carbon Emissions Trading in Beijing, retrived from http://www.bjets.com.cn/article/zcfg/201407/20140700000255.shtml. [2] Chau, C., Leung, T., Ng, W., 2015. A review on life cycle assessment, life cycle energy assessment and life cycle carbon emissions assessment on buildings. Applied Energy 143, 395-413. [3] Housing and Construction Bureau of Shenzhen Municipality, 2016. The benefits and risk assessment report for building carbon emissions trading in Shenzhen, China. [4] Human Settlements and Environment Commission of Shenzhen Municipality, 2016a. The implementation plan for regulatory inspection about pollution sources in Shenzhen, retrived from http://www.szhec.gov.cn/xxgk/ywgz/hjjc/201605/t20160529_3665543.htm. [5] Human Settlements and Environment Commission of Shenzhen Municipality, 2016b. The information disclosure column of regulatory about Shenzhen's key pollution sources. [6] Human Settlements and Environment Commission of Shenzhen Municipality, 2016, 2017. The publicity of government regulatory environmental inspection results of Shenzhen, retrived from http://www.szhec.gov.cn/xsdw/hjjczd/bmpt/. [7] Lee, K., Han, T.-W., 2016. How vulnerable is the emissions market to transaction costs?: An ABMS Approach. Energy Policy 90, 273-286. [8] Liu, Z., Guan, D., Wei, W., Davis, S.J., Ciais, P., Bai, J., Peng, S., Zhang, Q., Hubacek, K., Marland, G., Andres, R.J., Crawford-Brown, D., Lin, J., Zhao, H., Hong, C., Boden, T.A., Feng, K., Peters, G.P., Xi, F., Liu, J., Li, Y., Zhao, Y., Zeng, N., He, K., 2015. Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature 524, 335-338. [9] Shen, L., Song, X., Wu, Y., Liao, S., Zhang, X., 2016. Interpretive Structural Modeling based factor analysis on the implementation of Emission Trading System in the Chinese building sector. Journal of Cleaner Production 127, 214-227. [10] Shuai, C., Shen, L., Jiao, L., Wu, Y., Tan, Y., 2017. Identifying key impact factors on carbon emission: Evidences from panel and time-series data of 125 countries from 1990 to 2011. Applied Energy 187, 310-325. [11] Wang, Z., Zhao, J., Li, M., 2017. Analysis and optimization of carbon trading mechanism for renewable energy application in buildings. Renewable and Sustainable Energy Reviews 73, 435-451.