Contributions to sector-level carbon intensity change：an integrated decomposition analysis Qunwei Wanga, Ye Hangb, Bin Sub,, Peng Zhoua a College of Economics and Management, Nanjing University of Aeronautics and Astronautics bSchool of Business, Soochow University cEnergy Studies Institute, National University of Singapore

Outline Introduction Methodology Case study Conclusion

Introduction Driving factors？ Background Environmental-economic balance requirement of tackling climate change Carbon intensity (i.e. carbon emissions per unit of GDP) is an important metric for measuring energy and environmental performance Carbon intensity reduction targets China: 13th Five Year Plan (2016-2020), reduce carbon intensity by 18% compared to 2015 level (State Council, 2016) India: Intended Nationally Determined Contribution (INDC), reduce carbon intensity by 33-35% by 2030 from 2005 level (UNFCCC, 2015) Tackling climate change issue requires balancing economic growth and environmental sustainability. This requirement has made carbon intensity an important matric for measuring energy and environmental performance. Some countries have included it in their emission reduction targets. For example, the 13th Five Year Plan of China sets the reduction target of carbon intensity by 18% compared to 2015 level (State Council, 2016). The Intended Nationally Determined Contribution (INDC) announced by India pledges to reduce its carbon intensity by 33-35% by 2030 from 2005 level (UNFCCC,2015). An important foundation for effectively reducing carbon intensity is decomposing the driving factor of carbon intensity change. Driving factors？

Introduction Decomposition method Index decomposition analysis (IDA) (Ang and Zhang, 2000; Xu and Ang, 2013) Structural decomposition analysis (SDA) (Rose and Casler, 1996; Su and Ang, 2012) Attribution analysis (AA) (Choi and Ang, 2012; Su and Ang, 2014) Quantify the contributions of the individual components to an aggregate indicator change AA based on LMDI in IDA; AA based on generalized Fisher index in SDA The impacts of production technology related components cannot be directly investigated under the IDA and SDA frameworks Production-theoretical decomposition analysis (PDA) (Zhou and Ang, 2008) Production technology related components (technical efficiency & technological change) Potential effects deflated by technical efficiency PDA and IDA combined approach (Kim and Kim, 2012; Lin and Du, 2014) Production technology related components & Potential effects Avoid misleading results of PDA in quantifying industrial structure and energy mix effects In terms of decomposition method, IDA and SDA are two commonly used decomposition methods to quantify the driving factors in energy and environmental studies. As an extension to decomposition analysis, the attribution analysis method allows the contribution of the individual components to an aggregate indicator change to be quantified. However, the impacts of production technology related components can not be directly investigated under the IDA and SDA frameworks. To solve this problem, some studies have integrated the DEA technique and Shephard distances functions from production theory into the decomposition framework. The key features of the PDA method is to provide some production technology related components and the potential effects deflated by technical efficiency.

Introduction Summary of the studies combining PDA and IDA methods Study Indicator Application area SDF Variable Effect Level Period Input Output act cef pcef emx pei str e-pr y-pr c-pr sub ros pros gap 1. Kim and Kim (2012) C National 1990-2006 E Y, C √   2. Lin and Du (2014) EI Regional 2005-2010 Y E, K, L 3. Du and Lin (2015) 2003-2010 4. Du et al. (2017) 2006-2012 5. Li et al. (2017) 2001-2011 E, Y 6. Wang et al. (2017b) National/Regional/Sectoral E, C E, L 7. Zhou et al. (2017) National/ Sectoral 1991-2012/ 2001-2012 This study (PDA-IDA-AA) CI 2006-2014 The existing PDA and IDA combined approach cannot further capture the contribution of each region to the individual driving factor In the literature, 7 such studies can be found, and their indicators decomposed, application area, the orientation of Shephard distance function, variables, and the effects decomposed into are shown in this table. In fact, carbon intensity in a country is driven by its carbon intensity of each region. However, the existing PDA and IDA combined approach cannot further capture the contribution of each region to the individual driving factor. Note: The indicators “C”, “EI”, “E” and “CI” stand for the changes in carbon emissions, energy intensity, energy consumption and carbon intensity, respectively. The abbreviation “SDF” refers to Shephard distance function. The variables “E”, “K”, “L”, “Y” and “C” refer to energy, capital, labor, desirable output and undesirable output, respectively. The abbreviations “act”, “cef”, “pcef”, emx”, “pei”, “str”, “e-pr”, “y-pr”, “c-pr”, “sub”, “ros”, “pros” and “gap” refer to the various effects in the decomposition identity, i.e. activity, carbon emission coefficient, potential carbon emission coefficient, energy mix, potential energy intensity, economy structure, energy use productivity (i.e. energy use technical efficiency and energy use technological change), desirable output productivity (i.e. desirable output technical efficiency and desirable output technological change), carbon emission productivity, substitution, regional output structure, potential regional output structure and output gap effects, respectively.

Introduction Objective Contribution Proposing an integrated decomposition approach combining PDA, IDA and AA to explore the contributions to sector-level carbon intensity change Contribution Developing an integrated decomposition framework combining PDA, IDA and AA Creating two new pre-defined factors, i.e. the potential regional output structure effect and the output gap effect Focusing on the decomposition of carbon intensity, which is less well documented compared to other indicators Obtaining the decomposition results at the single sectoral level Therefore, the objective of our study is decomposing… The contributions of this study are as follows.

Outline Introduction Methodology Case study Conclusion

Methodology Environmental production technology (Zhou and Ang, 2008) Assume that the entire carbon intensity consists of I regions. Energy consumption, gross domestic production and carbon emissions are inputs, desirable outputs and undesirable outputs, respectively. The sector of region i uses finite inputs to generate desirable outputs and undesirable outputs. The environmental production technology is defined using the DEA linear programming under the consumption of constant returns to scale.

Methodology Shephard distance function and estimation models Shephard input distance function Shephard desirable output distance function Estimation models: DEA linear programming The Shephard input distance function, Shephard desirable output distance function and their DEA estimation model We adopt the DEA technique in Zhou and Ang (2008) to calculate the distance functions. The infeasibility issue may occur when solving mixed period distance functions, and we use the modified approach in Wang et al. (2015) to solve this issue. Feasible (Zhou and Ang, 2008) Infeasible (Wang et al., 2015)

Methodology Decomposition approach — PDA Emission coefficient, energy mix, energy intensity, regional output structure Five categories (nine components) 1. Structure Energy mix (EMX) Potential regional output structure (PIS) 2. Intensity Potential energy intensity (PEI) Emission coefficient (CEF) 3. Output gap Output gap (ISG) 4. Energy use productivity Energy use technical efficiency (EUE) Energy use technological change (EST) 5. Desirable output productivity Desirable output technical efficiency (YOE) Desirable output technological change (YCT) To identify the driving factors of carbon intensity change, the carbon intensity can be decomposed into Emission coefficient, energy mix, energy intensity, regional output structure. This identity can be transformed into a production-based decomposition model by combining the distance functions. This equation shows that changes in carbon intensity can be decomposed into five categories, including 9 components. The first category represents…including…

Methodology Decomposition approach — IDA Single-period Multi-period ＞1 ＝1 ＜1 The impact of each component can be calculated using the LMDI method in IDA Taking the potential energy intensity as an example, If the decomposition result is greater than unity, the potential energy intensity effect contribute to increase in carbon intensity from year t to year t+1. If the decomposition result is equal to unity, this effect does not affect carbon intensity during this period. If。。is less than unity, this effect contribute to derease in carbon intensity. Multi-period

Methodology Attribution analysis method (Choi and Ang, 2012) Single-period Multi-period The single-period contribution of the sector in region i to the potential energy intensity effect The multi-period contribution of the sector in region i to the potential energy intensity effect over the [0,T] period

Outline Introduction Methodology Case study Conclusion

Case study Data Variable Industrial sector across 30 provinces in China (2006-2014) China Energy Statistical Yearbook, 2007-2015 China Statistical Yearbook, 2007-2015 2006 IPCC Guidelines for National Greenhouse Gas Inventories Variable Energy consumption (E) Industrial output (Y) Carbon emissions (C) Using the 2006-2014 datasets of industrial sector across 30 provinces in China to illustrate the integrated decomposition approach.

Case study Results and discussion — Decomposition results Cumulative effects on industrial carbon intensity change by five categories, 2006-2014 Dtot = 47.63% 47.55% 52.18% The decomposition results show that the industrial carbon intensity in China decreased by 47.63%. Of the five decomposition categories, desirable output productivity and intensity are the leaders in promoting the reduction of industrial carbon Intensity. They contribute to industrial carbon intensity decrease of 52.18% and 47.55%, respectively.

Case study Results and discussion — Decomposition results Changes in industrial carbon intensity and its decomposition, 2006-2014 Of the nine decomposition components, desirable output technological change and potential energy intensity factors play dominant roles in decreasing the industrial carbon intensity. They contribute to carbon intensity decrease of 60.07% and 42.15%, respectively. Conversely, output gap is the main factor hindering decreases in industrial carbon intensity. It contribute to carbon intensity increase of 89.31%. 47.63% 42.15% 89.31% 60.07%

Case study Results and discussion — Decomposition results Cumulative changes in industrial carbon intensity and its decomposition, 2006-2014 In terms of evolutionary trends, the trends for desirable output technological change effect and the potential energy intensity effect were close to the trend associated with industrial carbon intensity. This further indicates that these two effects were leading factors in promoting decreased industrial carbon intensity. DPEI DYCT

Case study Results and discussion — Attribution results Multi-period attribution results of DYCT and DPEI, 2006-2014 (base: 2006, %) The cumulative contribution of desirable output technological change effect was -60.07%. The top five provinces contributing to this negative value (-60.07%) are marked in red. They are Hebei (-5.72%), Shandong (-5.39%), Jiangsu (-4.47%), Liaoning (-3.69%) and Henan (-3.41%). The cumulative contribution of potential energy intensity effect was -42.15%. The top five provinces contributing to this negative value (-42.15%) are marked in blue boxes, including Henan (-3.90%), Liaoning (-3.71%), Shandong (-3.18%), Hunan (-2.97%) and Inner Mongolia (-2.83%).

Case study Results and discussion — Attribution results Single-period attribution results of DYCT, 2006-2014 (base: previous year, %) Province 2007 2008 2009 2010 2011 2012 2013 2014 Mean Beijing -0.09 0.08 -0.18 -0.16 -0.07 -0.04 -0.08 Tianjin -0.10 -0.12 0.14 -0.33 -0.42 -0.21 -0.19 Hebei -0.53 -0.65 0.69 -1.69 -1.84 -1.29 -1.57 -1.52 -1.05 Shanxi -0.31 -0.37 0.38 -0.86 -0.88 -0.63 -0.81 -0.20 -0.46 Inner Mongolia -0.45 1.63 0.45 -1.08 -1.37 -3.29 -0.11 -0.69 Liaoning -0.54 0.58 -1.31 -1.18 -0.76 -0.94 -0.25 -0.62 Jilin -0.24 0.21 -0.41 -0.60 -0.06 Heilongjiang 0.20 -0.39 -0.13 Shanghai 0.19 -0.35 -0.32 Jiangsu -0.74 -0.68 0.70 -1.76 -0.56 -1.41 -0.26 -0.75 Zhejiang -0.61 1.31 0.18 -1.72 -0.82 -0.66 0.02 Anhui 0.25 -0.67 -0.30 -0.57 Fujian -0.23 0.27 -0.59 Jiangxi -0.14 -0.34 -0.43 -0.38 Shandong -0.79 -0.83 0.88 -1.62 -2.10 -1.75 -0.91 Henan -0.55 0.60 -1.32 -0.99 -0.15 Hubei -0.28 0.36 -0.73 -1.14 -0.44 -0.85 Hunan 0.37 -0.64 -0.27 Guangdong 0.91 -1.01 -1.06 0.16 Guangxi -0.22 -0.51 -0.17 -0.50 Hainan -0.01 -0.02 -0.05 Chongqing -0.36 -0.49 Sichuan 0.35 -1.11 -0.47 Guizhou Yunnan 0.00 Shaanxi 0.17 -0.40 Gansu Qinghai 0.40 0.31 1.16 0.11 Ningxia -0.78 0.03 Xinjiang Total change -8.55 -5.34 8.77 -19.94 -21.91 -11.54 -20.51 -3.53 -10.32 The annual average contribution of desirable output technological change effect was -10.32%. The contribution of the desirable output technological change effect significantly declined to -21.91% in 2011, mainly due to Shandong (-2.10%).

Case study Results and discussion — Attribution results Single-period attribution results of DPEI, 2006-2014 (base: previous year, %) Province 2007 2008 2009 2010 2011 2012 2013 2014 Mean Beijing -0.12 -0.22 -0.02 -0.04 -0.05 -0.01 -0.03 -0.06 Tianjin -0.07 -0.14 0.20 -0.17 -0.09 -0.10 0.08 Hebei -0.42 -2.05 1.25 -0.82 -1.28 1.97 0.47 Shanxi -0.71 -1.32 0.49 -0.98 -0.67 0.22 1.62 0.58 Inner Mongolia -0.79 -0.88 -0.32 -1.05 -0.21 -0.53 0.07 0.34 Liaoning -0.89 -1.77 0.80 -1.36 -1.67 -0.35 1.04 -0.11 -0.54 Jilin -0.46 -0.48 -0.51 -0.15 Heilongjiang -0.49 0.59 -0.63 -0.40 0.09 Shanghai 0.00 0.01 0.03 0.32 0.02 Jiangsu -0.58 -0.66 -1.09 1.03 0.16 Zhejiang -0.36 -0.57 -0.55 -0.50 0.10 -0.08 -0.23 Anhui -0.60 0.19 -0.26 Fujian -0.19 -0.16 -0.41 -0.24 -0.27 0.05 Jiangxi -0.33 0.11 -0.83 0.45 Shandong -0.77 -1.41 0.30 -0.80 0.15 Henan -0.75 -1.54 0.53 -1.02 -1.30 -0.62 Hubei -0.78 -0.95 Hunan -0.52 -0.18 Guangdong -0.20 -0.28 0.06 -0.25 -0.13 Guangxi -0.39 0.14 -0.59 Hainan 0.04 Chongqing -0.47 -0.43 1.86 -2.17 0.13 Sichuan -0.64 -0.84 -1.22 1.31 Guizhou -0.34 0.27 -0.30 Yunnan 0.40 Shaanxi Gansu -0.38 0.24 Qinghai Ningxia Xinjiang 0.64 0.54 0.26 Total change -10.13 -17.53 4.42 -15.83 -13.02 -1.64 2.70 1.08 -6.24 The annual average contribution of potential energy intensity effect was -6.24%. The contribution of this effect significantly fell in 2008 and 2010, to -17.53% and -15.83%, respectively. In 2008, Hebei (-2.05%) contributed the most to the decrease in this effect. In 2010, Liaoning (-1.36)contributed the most to the decrease in this effect. In contrast, the contribution of the potential energy intensity effect significantly increased to 4.42% in 2009, mainly due to Hebei (1.25%) and Liaoning (0.80%).

Case study Results and discussion — Attribution results Percentage share of each province in the multi-period attribution results of DYCT and DPEI, 2006-2014 (%) high low high high Some provinces had a higher percentage share than other both desirable output technological change and potential energy intensity. This indicates that these provinces performed better in these two effects For example, the percentage of Shandong in Dyct is 8.79%, and its percentage share in Dpei is 7.46%. Besides, some provinces had a relatively higher percentage share in the desirable output technological change effect, but contributed relatively little to the potential energy intensity effect, and vice versa. For example, the percentage share of Guangdong in Dyct is 4.59%,which is larger the than average, while its percentage share in Dpei is 1.79%. This reflects that differentprovinces preferred different approaches for realizing industrial carbon intensity reduction target. It also provides guidance to further decline industrial carbon intensity for different provinces in the future.

Case study Results and discussion — Attribution results Classification of provinces based on percentage share of each province in the multi-period attribution results of DYCT and DPEI, 2006-2014 Type Ⅰ (8 provinces) Type Ⅱ (2 provinces) Type Ⅲ (14 provinces) Type Ⅳ (6 provinces) All provinces were divided into four different types based on the different percentage share of each province for the technological change effect and the potential energy intensity effect. For example, the 8 Type I provinces in the first quadrant serve as benchmarks for other provinces seeking to promote decreases in industrial carbon intensity.

Outline Introduction Methodology Case study Conclusion

Conclusion Contribution The integrated decomposition approach extends the existing PDA and IDA combined approach by quantifying the contribution of each region attributes to the individual driving factor using the AA method. This helps set carbon intensity reduction policies targeting a specific factor at the regional level. The decomposition model adds two new effects, i.e. the potential regional output structure effect and the output gap effect. This may provide insights to different aspects of carbon intensity reduction policymaking. This study focuses on decomposing sector-level carbon intensity change, which is relatively less well documented and understood compared to other indicators/levels in the existing PDA and IDA combined studies.

Conclusion Case study Of the five decomposition categories, desirable output productivity and intensity are the leaders in promoting the reduction of industrial carbon intensity. Of the nine decomposition components, desirable output technological change and potential energy intensity factors play dominant roles in decreasing the industrial carbon intensity. The main contributors to the negative value of the desirable output technological change effect are Hebei, Shandong, Jiangsu, Liaoning and Henan. The top five provinces contributing to the negative value of the potential energy intensity effect are Henan, Liaoning, Shandong, Henan and Inner Mongolia. Provinces were divided into four types based on the multi-period attribution results; different industrial carbon intensity reduction policies should be implemented in different types of provinces.

Conclusion- Extension Application Sector-level Economy-wide Other absolute and intensity indicators, e.g. energy, water, air emissions and waste Carbon intensity Methodology IDA framework SDA framework Temporal PDA-IDA/SDA-AA Spatial (Ang et al., 2015; Su and Ang, 2016 ) Spatial-Temporal (Ang et al., 2016)

Bin Su (subin@nus.edu.sg ; subin.nus@gmail.com) Thank you! Qunwei Wang (wqw0305@126.com) Bin Su (subin@nus.edu.sg ; subin.nus@gmail.com)