Analysis on Spatial Pattern and Driving Factors of Carbon Emission in Urban–Rural Fringe Mixed-Use Communities: Cases Study in East Asia
Abstract
:1. Introduction
2. Literature Review
2.1. Low Carbon Research Areas and Scales
2.2. Methods for Carbon Emission Research
2.3. Low-Carbon Community Research
3. Cases and Methods
3.1. Cases Selection
3.2. Methods
3.2.1. Spatial Grid Analysis
3.2.2. IPCC-EF
3.2.3. Partial Least Squares Regression
4. Results
4.1. Carbon Emissions and Spatial Distribution Analysis
4.2. Carbon Flow Billing Analysis
4.3. Factors Affecting Carbon Emissions
5. Recommendation
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Intergovernmental Panel on Climate Change. Global Warming of 1.5 °C: An. IPCC Special Report on the Impacts of Global Warming of 1.5 °C Above Pre-industrial Levels and Related Global Greenhouse Gas. Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty; Intergovernmental Panel on Climate Change: Geneva, Switzerland, 2018. [Google Scholar]
- Green, F.; Nicholas, S. China’s changing economy: Implications for its carbon dioxide emissions. Clim. Policy. 2017, 17, 423–442. [Google Scholar] [CrossRef] [Green Version]
- Rosa, E.A.; Dietz, T. Human drivers of national greenhouse-gas emissions. Nat. Clim. Chang. 2012, 2, 581–586. [Google Scholar] [CrossRef]
- Diffenbaugh, N.S.; Singh, D.; Mankin, J.S.; Horton, D.E.; Swain, D.L.; Touma, D.; Tsiang, M. Quantifying the influence of global warming on unprecedented extreme climate events. Proc. Natl. Acad. Sci. USA 2017, 114, 4881–4886. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hansen, J.; Sato, M.; Hearty, P.; Ruedy, R.; Kelley, M.; Masson-Delmotte, V.; Rignot, E. Ice melt, sea level rise and superstorms: Evidence from paleoclimate data, climate modeling, and modern observations that 2 C global warming could be dangerous. Atmos. Chem. Phys. 2016, 16, 3761–3812. [Google Scholar] [CrossRef] [Green Version]
- Ginsburg, N. Extended metropolitan regions in Asia: A new spatial paradigm. In The Extended Metropolis: Settlement Transition in Asia; University of Hawaii Press: Honolulu, HI, USA, 1991; pp. 27–46. [Google Scholar]
- Sui, D.Z.; Zeng, H. Modeling the dynamics of landscape structure in Asia’s emerging desakota regions: A case study in Shenzhen. Landsc. Urban Plan. 2001, 53, 37–52. [Google Scholar] [CrossRef]
- Legates, R.; Hudalah, D. Peri-urban planning for developing East Asia: Learning from chengdu, China and Yogyakarta/Kartamantul, Indonesia. J. Urban. Aff. 2014, 36 (Supppl. 1), 334–353. [Google Scholar] [CrossRef]
- Firman, T. Post-suburban elements in an Asian extended metropolitan region: The case of Jabodetabek (Jakarta Metropolitan Area). In International Perspectives on Suburbanization; Springer: Berlin/Heidelberg, Germany, 2011. [Google Scholar]
- Lin, G.C. Evolving spatial form of urban-rural interaction in the Pearl River Delta, China. Prof. Geogr. 2001, 53, 56–70. [Google Scholar] [CrossRef]
- Hudalah, D.; Winarso, H.; Woltjer, J. Peri-urbanisation in East Asia: A new challenge for planning? Int. Dev. Plan. Rev. 2007, 29, 503–519. [Google Scholar] [CrossRef]
- Igusa, K. Globalization in Asia and Local Revitalization Efforts: A view from one village one product (OVOP) movement in Oita. Retr. Feb. 2006, 18, 2015. [Google Scholar]
- Shi, L. A Search for Alternative Developmental Paradigm: New village movement in South Korea and rural construction in China. Sociol. Res. 2004, 4, 39–49. [Google Scholar]
- Côté, R.P.; Cohen-Rosenthal, E. Designing eco-industrial parks: A synthesis of some experiences. J. Clean. Prod. 1998, 6, 181–188. [Google Scholar] [CrossRef] [Green Version]
- Hachem-Vermette, C.; Cubi, E.; Bergerson, J. Energy performance of a solar mixed-use community. J. Sustain. Cities Soc. 2016, 27, 145–151. [Google Scholar] [CrossRef]
- Hertwich, E.G.; Peters, G.P. Carbon footprint of nations: A global, trade-linked analysis. Environ. Sci. Technol. 2009, 43, 6414–6420. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Druckman, A.; Jackson, T. The carbon footprint of UK households 1990–2004: A socio-economically disaggregated, quasi-multi-regional input–output model. Ecol. Econom. 2009, 68, 2066–2077. [Google Scholar] [CrossRef] [Green Version]
- Lin, S.; Wang, S.; Marinova, D.; Zhao, D.; Hong, J. Impacts of urbanization and real economic development on CO2 emissions in non-high income countries: Empirical research based on the extended STIRPAT model. J. Clean. Prod. 2017, 166, 952–966. [Google Scholar] [CrossRef]
- Wang, S.; Liu, X. China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces. Appl. Energy. 2017, 200, 204–214. [Google Scholar] [CrossRef]
- Dahal, K.; Juhola, S.; Niemelä, J. The role of renewable energy policies for carbon neutrality in Helsinki Metropolitan area. Sustain. Cities Soc. 2018, 40, 222–232. [Google Scholar] [CrossRef] [Green Version]
- Gustavsson, L.; Joelsson, A.; Sathre, R. Life cycle primary energy use and carbon emission of an eight-storey wood-framed apartment building. Energy Build. 2010, 42, 230–242. [Google Scholar] [CrossRef]
- Li, J.; Liang, X.; Cockerill, T. Getting ready for carbon capture and storage through a ‘CCS (Carbon Capture and Storage) Ready Hub’: A case study of Shenzhen city in Guangdong province, China. Energy 2011, 36, 5916–5924. [Google Scholar] [CrossRef]
- Feng, K.; Siu, Y.L.; Guan, D.; Hubacek, K. Analyzing drivers of regional carbon dioxide emissions for China: A structural decomposition analysis. J. Ind. Ecol. 2012, 16, 600–611. [Google Scholar] [CrossRef]
- Cansino, J.M.; Sánchez-Braza, A.; Rodríguez-Arévalo, M.L. Driving forces of Spain’ s CO2 emissions: A LMDI decomposition approach. Renew. Sustain. Energy Rev. 2015, 48, 749–759. [Google Scholar] [CrossRef]
- Wang, C.; Wang, F.; Zhang, X.; Yang, Y.; Su, Y.; Ye, Y.; Zhang, H. Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang. Renew. Sustain. Energy Rev. 2017, 67, 51–61. [Google Scholar] [CrossRef]
- Feng, Y.; Chen, S.; Zhang, L. System dynamics modeling for urban energy consumption and CO2 emissions: A case study of Beijing, China. Ecol. Model. 2013, 252, 44–52. [Google Scholar] [CrossRef]
- Ramaswami, A.; Main, D.; Bernard, M.; Chavez, A.; Davis, A.; Thomas, G.; Schnoor, K. Planning for low-carbon communities in US cities: A participatory process model between academic institutions, local governments and communities in Colorado. Carb. Manag. 2011, 2, 397–411. [Google Scholar] [CrossRef] [Green Version]
- Chatterton, P. Towards an agenda for post-carbon cities: Lessons from Lilac, the UK’s first ecological, affordable cohousing community. Int. J. Urban Reg. Res. 2013, 37, 1654–1674. [Google Scholar] [CrossRef] [Green Version]
- Charoenkit, S.; Kumar, S. Environmental sustainability assessment tools for low carbon and climate resilient low income housing settlements. Renew. Sustain. Energy Rev. 2014, 38, 509–525. [Google Scholar] [CrossRef]
- Wang, W.; Yang, J.; Muntz, R. STING: A statistical information grid approach to spatial data mining. In Proceedings of the VLDB 1997, Athens, Greece, 25–29 August 1997. [Google Scholar]
- Ye, J.; Yang, X.; Jiang, D.; Urban, M. The grid scale effect analysis on town leveled population statistical data spatialization. J. Geo-Inform. Sci. 2010, 12, 40–46. [Google Scholar] [CrossRef]
- Borzacchiello, M.T.; Nijkamp, P.; Koomen, E. Accessibility and urban development: A grid-based comparative statistical analysis of Dutch cities. Environ. Plan. B Plan. Des. 2010, 37, 148–169. [Google Scholar] [CrossRef] [Green Version]
- IPCC. 2006 IPCC guidelines for National Greenhouse Gas Inventories; IPCC: Geneva, Switzerland, 2006. [Google Scholar]
- Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: Thousand Oaks, CA, USA, 2016. [Google Scholar]
- Wang, Z.; Yang, L. Indirect carbon emissions in household consumption: Evidence from the urban and rural area in China. J. Clean. Prod. 2014, 78, 94–103. [Google Scholar] [CrossRef]
- Kaya, Y. Impact of carbon dioxide emission on GNP growth: Interpretation of proposed scenarios, presentation to the energy and industry subgroup, response strategies working group. In Proceedings of the IPCC Conference, Paris, France, 30 June 1989. [Google Scholar]
- Li, H.; Mu, H.; Zhang, M.; Gui, S. Analysis of regional difference on impact factors of China’s energy–Related CO2 emissions. Energy 2012, 39, 319–326. [Google Scholar] [CrossRef]
- Wang, Z.; Yin, F.; Zhang, Y.; Zhang, X. An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China. Appl. Energy 2012, 100, 277–284. [Google Scholar] [CrossRef]
- Galeotti, M.; Lanza, A.; Pauli, F. Reassessing the environmental Kuznets curve for CO2 emissions: A robustness exercise. Ecol. Econom. 2006, 57, 152–163. [Google Scholar] [CrossRef]
- Le Quéré, C.; Andrew, R.; Friedlingstein, P.; Sitch, S.; Pongratz, J.; Manning, A.; Jackson, R. Global Carbon Budget 2017. Earth Syst. Sci. Data 2018, 10, 405–448. [Google Scholar] [CrossRef] [Green Version]
No. | Case Location | Landform | Type | Development Degree | Built-Up Density |
---|---|---|---|---|---|
A-1 | Songhu, China | Plain | Industrial | MID | 72.5% |
A-2 | Shanhuli, China | Riverland | Industrial | MID | 71.8% |
A-3 | Liushi, China | Riverland | Industrial | LATE | 79.6% |
A-4 | Kusu district, Japan | Riverland | Industrial | LATE | 62.8% |
A-5 | Xucun, China | Plain | Industrial | LATE | 78.6% |
B-1 | Dafen, China | Plain | Commercial | MID | 59.2% |
B-2 | Jiujiang, China | Riverland | Commercial | LATE | 83.5% |
B-3 | Chuangjiang, China | Basin | Commercial | MID | 68.2% |
B-4 | Jiayi, China Taiwan | Plain | Commercial | LATE | 73.1% |
B-5 | Dali Art, China, Taiwan | Plain | Commercial | MID | 68.1% |
C-1 | Muikamachi, Japan | Riverland | Tourism | LATE | 70.6% |
C-2 | Gamcheon, Korea | Hill | Tourism | LATE | 65.3% |
C-3 | Longwu, China | Hill | Tourism | INITIAL | 50.16% |
C-4 | Yucun, China | Hill | Tourism | MID | 73.2% |
C-5 | North Village, Korea | Hill | Tourism | MID | 70.6% |
D-1 | Mailiao, China, Taiwan | Hill | Rental-apartment | MID | 62.1% |
D-2 | Gonguan, China, Taiwan | Hill | Rental-apartment | MID | 71.3% |
D-3 | Chengyiyuan, China | Basin | Rental-apartment | LATE | 76.2% |
D-4 | Luojiazhuang, China | Riverland | Rental-apartment | MID | 64.8% |
D-5 | Putuoxishan, China | Coast | Rental-apartment | INITIAL | 52.1% |
Primary Indicator | Secondary Indicators | Independent Variable |
---|---|---|
Established space index | Built-up density (%) | X1 |
Municipal distribution (㎡) | X2 | |
Floor area ratio | X3 | |
Green rate (%) | X4 | |
Industrial economic index | Per capita economic income ($) | X5 |
Leading industry concentration (%) | X6 | |
GDP per unit land ($/ha) | X7 | |
Material consumption index | Electricity consumption per unit land (kilowatt-hour /ha) | X8 |
Water consumption per unit land (t/ha) | X9 | |
Fuel consumption per unit land (kJ/ha) | X10 | |
Production and living material consumption per unit land (t/ ha) | X11 | |
Social behavior index | Population fluctuation rate (%) | X12 |
Population per unit land | X13 | |
Employment-to-living ratio (%) | X14 | |
Green traffic ratio per capita (%) | X15 |
VAR ID | VIP |
---|---|
Fuel energy consumption per unit of land | 1.4932 |
Electricity consumption per unit of land | 1.43228 |
Built-up density | 1.19309 |
Floor area rate | 1.15503 |
Employment-to-Living Ratio | 1.13881 |
Production and living materials per unit of land | 1.07908 |
Population per unit of land | 0.955784 |
Green rate | 0.955784 |
Green transportation ratio per capita | 0.896847 |
Water consumption per unit of land | 0.872799 |
GDP per soil unit | 0.867382 |
Municipal distribution | 0.833209 |
leading industries concentration | 0.639332 |
Population fluctuation rate | 0.638946 |
Economic income per capita | 0.551304 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhu, X.; Zhang, T.; Gao, W.; Mei, D. Analysis on Spatial Pattern and Driving Factors of Carbon Emission in Urban–Rural Fringe Mixed-Use Communities: Cases Study in East Asia. Sustainability 2020, 12, 3101. https://doi.org/10.3390/su12083101
Zhu X, Zhang T, Gao W, Mei D. Analysis on Spatial Pattern and Driving Factors of Carbon Emission in Urban–Rural Fringe Mixed-Use Communities: Cases Study in East Asia. Sustainability. 2020; 12(8):3101. https://doi.org/10.3390/su12083101
Chicago/Turabian StyleZhu, Xiaoqing, Tiancheng Zhang, Weijun Gao, and Danying Mei. 2020. "Analysis on Spatial Pattern and Driving Factors of Carbon Emission in Urban–Rural Fringe Mixed-Use Communities: Cases Study in East Asia" Sustainability 12, no. 8: 3101. https://doi.org/10.3390/su12083101