Key driving forces on the development of low carbon city (LCC) in China

Abstract Developing low carbon city (LCC) has been widely appreciated as the key strategy for achieving emission mitigation goal. Despite the impact factors on cities’ carbon emission reduction have been extensively studied in recent years, little attention has been paid to investigating the key driving force (KDF) on the development of LCC. This study aims to explore the KDF affecting LCC within the context of China through identifying systematically representative forces (RFs) and examining the interactive relationships between RFs. Based on topic modeling of Latent Dirichlet Allocation (LDA), RFs for LCC are identified firstly from the policies issued by the pilot LCCs in China. Then KDFs for LCC are investigated through establishing the interactive relationships between RFs by adopting the Interpretive Structure Modeling (ISM). Results show that: (1) 16 RFs on LCC are identified from the pilot LCC policies in China; (2) emission trading system, carbon emission accounting system, and low carbon planning are identified as the KDFs on the development of LCC; (3) carbon sink is positioned at the topmost level and performs as the most frontier force on the development of LCC. Results of the KDFs identification can provide policy-makers with insight for designing LCC improvement path, and consequently promote emission mitigation in China.

[1]  Xianchun Tan,et al.  China’s regional CO2 emissions reduction potential: A study of Chongqing city , 2016 .

[2]  Jing Wu,et al.  Policies to enhance the drivers of green housing development in China , 2018, Energy Policy.

[3]  Wenqiang Dai,et al.  Topic analysis of online reviews for two competitive products using latent Dirichlet allocation , 2018, Electron. Commer. Res. Appl..

[4]  Min Zhao,et al.  Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method , 2010 .

[5]  Kangyin Dong,et al.  Determinants of the global and regional CO2 emissions: What causes what and where? , 2019, Applied Economics.

[6]  Kevin Lo,et al.  China's low-carbon city initiatives: The implementation gap and the limits of the target responsibility system , 2014 .

[7]  John Elder,et al.  Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications , 2012 .

[8]  Alan L. Porter,et al.  Clustering scientific documents with topic modeling , 2014, Scientometrics.

[9]  Yanan Wang,et al.  Research on impacts of population-related factors on carbon emissions in Beijing from 1984 to 2012 , 2015 .

[10]  Peng Lin,et al.  A topic modeling based bibliometric exploration of hydropower research , 2016 .

[11]  Nan Li,et al.  Analysis on influence factors of China's CO2 emissions based on Path–STIRPAT model , 2011 .

[12]  Xiaoyan Li,et al.  Research on three-stage dynamic relationship between carbon emission and urbanization rate in different city groups , 2018, Ecological Indicators.

[13]  Liyin Shen,et al.  A new panel for analyzing the impact factors on carbon emission: A regional perspective in China , 2019, Ecological Indicators.

[14]  Tong Wu,et al.  A holistic overview of the progress of China’s low-carbon city pilots , 2018, Sustainable Cities and Society.

[15]  Liang Dong,et al.  Promoting low-carbon city through industrial symbiosis: A case in China by applying HPIMO model , 2013 .

[16]  Sheng Tang,et al.  A density-based method for adaptive LDA model selection , 2009, Neurocomputing.

[17]  Jian Zuo,et al.  Exploring the impact of urbanization on urban building carbon emissions in China: Evidence from a provincial panel data model , 2020 .

[18]  Steffen Lehmann,et al.  Low-to-no carbon city: Lessons from western urban projects for the rapid transformation of Shanghai , 2013 .

[19]  Rattan Lal,et al.  Low-carbon agriculture in South America to mitigate global climate change and advance food security. , 2017, Environment international.

[20]  Prakash Loungani,et al.  Decoupling of Emissions and GDP: Evidence from Aggregate and Provincial Chinese Data , 2018, Energy Economics.

[21]  Jing Shuai,et al.  Topic modelling of ecology, environment and poverty nexus: An integrated framework , 2018, Agriculture, Ecosystems & Environment.

[22]  Jin Yang,et al.  A holistic low carbon city indicator framework for sustainable development , 2017 .

[23]  David Fridley,et al.  China's pilot low-carbon city initiative: A comparative assessment of national goals and local plans , 2014 .

[24]  Bo Li,et al.  Using the STIRPAT model to explore the factors driving regional CO2 emissions: a case of Tianjin, China , 2015, Natural Hazards.

[25]  O. Edenhofer,et al.  Mitigation from a cross-sectoral perspective , 2007 .

[26]  Liwei Liu,et al.  China׳s carbon-emissions trading: Overview, challenges and future , 2015 .

[27]  Jian Zuo,et al.  Barriers to the transition towards off-site construction in China: An Interpretive structural modeling approach , 2018, Journal of Cleaner Production.

[28]  T. Roca,et al.  Human development Report 2013. The Rise of the South, Human Progress in a Diverse World , 2013 .

[29]  Rajib Sinha,et al.  Evaluating low-carbon city initiatives from the DPSIR framework perspective , 2015 .

[30]  Weisheng Lu,et al.  Decoupling relationship between economic output and carbon emission in the Chinese construction industry , 2018, Environmental Impact Assessment Review.

[31]  Nannan Wang,et al.  The evolution of low-carbon development strategies in China , 2014 .

[32]  Ye Qi,et al.  Developing low-carbon cities through pilots , 2015 .

[33]  Shomik Raj Mehndiratta,et al.  Sustainable Low-Carbon City Development in China , 2012 .

[34]  Baogui Xin,et al.  Scenario analysis of carbon emissions' anti-driving effect on Qingdao's energy structure adjustment with an optimization model, Part I: Carbon emissions peak value prediction , 2018 .

[35]  Vijay Nehra,et al.  Identification and analysis of barriers in implementation of solar energy in Indian rural sector using integrated ISM and fuzzy MICMAC approach , 2016 .

[36]  Xiaoling Zhang,et al.  Delivering a low-carbon community in China: Technology vs. strategy? , 2013 .

[37]  Yalin Lei,et al.  Analysis of the impact path on factors of China’s energy-related CO2 emissions: a path analysis with latent variables , 2017, Environmental Science and Pollution Research.

[38]  Guilin Qi,et al.  Detecting bursts in sentiment-aware topics from social media , 2018, Knowl. Based Syst..

[39]  Yi Yang,et al.  The decoupling effect and driving factors of carbon footprint in megacities: The case study of Xi’an in western China , 2019, Sustainable Cities and Society.

[40]  Shenghui Cui,et al.  A model for developing a target integrated low carbon city indicator system: The case of Xiamen, China , 2014 .

[41]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[42]  He Xu,et al.  A review of China’s carbon trading market , 2018, Renewable and Sustainable Energy Reviews.

[43]  Yutao Wang,et al.  Cities: The core of climate change mitigation , 2019, Journal of Cleaner Production.

[44]  Liyin Shen,et al.  Interpretive Structural Modeling based factor analysis on the implementation of Emission Trading System in the Chinese building sector , 2016 .

[45]  Toshihiko Masui,et al.  Aligning renewable energy targets with carbon emissions trading to achieve China's INDCs: A general equilibrium assessment , 2018 .

[46]  Liyin Shen,et al.  What drives the carbon emission in the Chinese cities?—A case of pilot low carbon city of Beijing , 2018 .

[47]  Yang Bai,et al.  Impact of land use and climate change on water-related ecosystem services in Kentucky, USA , 2019, Ecological Indicators.

[48]  Jie Lin,et al.  Policies and Practices of Low Carbon City Development in China , 2013 .

[49]  Yongtao Tan,et al.  Identifying key impact factors on carbon emission: Evidences from panel and time-series data of 125 countries from 1990 to 2011 , 2017 .

[50]  Jinhua Cheng,et al.  Can low-carbon city construction facilitate green growth? Evidence from China's pilot low-carbon city initiative , 2019, Journal of Cleaner Production.

[51]  Liyin Shen,et al.  The application of low-carbon city (LCC) indicators—A comparison between academia and practice , 2019, Sustainable Cities and Society.

[52]  Luigi Aldieri,et al.  Climate change and knowledge spillovers for cleaner production: New insights , 2020, Journal of Cleaner Production.

[53]  Andrew T. Karl,et al.  A practical guide to text mining with topic extraction , 2015 .

[54]  H. Schroeder,et al.  Methodology and applications of city level CO2 emission accounts in China , 2017 .

[55]  Nikhil Dev,et al.  Interpretive Structural Modelling (ISM) approach: An Overview , 2013 .

[56]  Ke Chen,et al.  Barriers to Building Information Modeling (BIM) implementation in China's prefabricated construction: An interpretive structural modeling (ISM) approach , 2019, Journal of Cleaner Production.

[57]  D. Fridley,et al.  ELITE cities: A low-carbon eco-city evaluation tool for China , 2015 .

[58]  Yulong Li,et al.  Developing interpretive structural modeling based on factor analysis for the water-energy-food nexus conundrum. , 2019, The Science of the total environment.

[59]  Yan Zhang,et al.  Estimation of energy-related carbon emissions in Beijing and factor decomposition analysis , 2013 .

[60]  Low carbon city: a guidebook for city planner and practitioners , 2013 .

[61]  R. Li,et al.  A spatio-temporal analysis of low carbon development in China’s 30 provinces: A perspective on the maximum flux principle , 2018, Ecological Indicators.

[62]  Kunhui Ye,et al.  Perceptions of governments towards mitigating the environmental impacts of expressway construction projects: A case of China , 2019, Journal of Cleaner Production.

[63]  Hossny Azizalrahman,et al.  Towards a generic multi-criteria evaluation model for low carbon cities , 2018 .

[64]  Baojun Tang,et al.  Price drivers in the carbon emissions trading scheme: Evidence from Chinese emissions trading scheme pilots , 2021 .

[65]  Fu Chen,et al.  Integrative analysis of carbon structure and carbon sink function for major crop production in China’s typical agriculture regions , 2017 .

[66]  Xiaoping Liu,et al.  Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities , 2017 .

[67]  Kangyin Dong,et al.  Driving forces and mitigation potential of global CO2 emissions from 1980 through 2030: Evidence from countries with different income levels. , 2019, The Science of the total environment.

[68]  Zhaohua Wang,et al.  Overview of research on China's transition to low-carbon development: The role of cities, technologies, industries and the energy system , 2018 .

[69]  Yue-Jun Zhang,et al.  Regional allocation of carbon emission quotas in China: Evidence from the Shapley value method , 2014 .

[70]  Rakesh D. Raut,et al.  To identify the critical success factors of sustainable supply chain management practices in the context of oil and gas industries: ISM approach , 2017 .

[71]  Zhaohua Wang,et al.  An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China , 2012 .

[72]  Jian Zuo,et al.  How national policies facilitate low carbon city development: A China study , 2019, Journal of Cleaner Production.

[73]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[74]  Margot Weijnen,et al.  Developing robust organizational frameworks for Sino-foreign eco-cities: comparing Sino-Dutch Shenzhen Low Carbon City with other initiatives , 2013 .

[75]  Richard H. Watson,et al.  Interpretive structural modeling—A useful tool for technology assessment? , 1978 .

[76]  Yongtao Tan,et al.  Identifying the key impact factors of carbon emission in China: Results from a largely expanded pool of potential impact factors , 2018 .