Evaluation of Water–Carbon–Ecological Footprint and Its Spatial–Temporal Changes in the North China Plain
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Data Sources
3. Methodology
3.1. Calculation of Water Footprint
3.2. Calculation of Carbon Footprint
3.3. Calculation of the Ecological Footprint
3.4. Coupling Coordination Analysis of Water–Carbon–Ecological Footprint
4. Results and Discussion
4.1. Characteristics of Changes in Total Water, Carbon and Ecological Footprint in the NCP
4.2. Characterization of Changes in the Composition of the Water, Carbon, and Ecological Footprint
4.3. Analysis of the Coupling Coordination Degree of Water–Carbon–Ecological Footprint
4.4. Policy Implementation
- (1)
- It is imperative to enhance the agricultural water management capacity in the NCP. The water footprint of the NCP is generally on an increasing trend, with agriculture accounting for the largest share of water demand, which is already more than 50% of the total water demand. Thus, water conservation measures need to be taken during agricultural production, such as encouraging the use of highly efficient irrigation methods such as drip systems and micro-irrigation, or the use of groundwater for sustainable irrigation [49]. Specific attention should be directed towards implementing water-saving measures in Henan and Shandong, where high levels of water consumption for agricultural purposes are particularly pronounced.
- (2)
- The adoption of clean energy sources to mitigate carbon emissions within the North China Plain represents a critical strategy for environmental conservation. Our study has documented a persistent rise in the carbon footprint of the region, which has escalated from 4.2 × 108 t to 1.69 × 109 t. Notably, coke and crude oil have been major contributors to carbon emissions, accounting for proportions ranging between 32.99% and 43.34% and 31.55% and 40.29%, respectively. By referencing the carbon content per unit of calorific value for each fossil energy source as presented in Table 1, it becomes apparent that LPG (liquefied petroleum gas) and petroleum emit significantly less carbon—17.2 tC/TJ and 15.32 tC/TJ, respectively—than coke (29.42 tC/TJ) and crude oil (20.08 tC/TJ) when generating equivalent calorific value. Consequently, we advocate for the aggressive promotion of clean energy alternatives, such as natural gas, as part of a broader initiative to drive industrial restructuring and foster green production methodologies. These measures are essential for achieving society’s ambitious goals of carbon peaking and carbon neutrality.
- (3)
- Enhancing land utilization efficiency within the North China Plain region is a paramount objective. Our study has identified that the ecological footprint in Henan Province and Shandong Province is comparatively substantial. Notably, the ecological footprint attributed to arable land constitutes the predominant share, underscoring the critical role of arable land as a vital component of the region’s land resources. In light of these findings, there is a compelling need to vigorously advance the construction of high-standard farmland. This initiative aims to elevate the quality of arable land in the North China Plain and to integrate the deployment of sophisticated agricultural technologies. Concurrently, it is observed that the ecological footprints of forests and watersheds contribute only a minor fraction to the overall ecological footprint. This necessitates a strategic approach to the more efficient utilization of land resources in the North China Plain, thereby ensuring the sustainable and harmonized development of natural resources within the region.
4.5. Limitations and Future Work
5. Conclusions
- (1)
- The overall trend of the water footprint in the NCP shows an initial increase followed by a decrease, with the growth rate gradually slowing down, and finally tending to stabilize. In 2020, it reached 6.52 × 1011 m3. Shandong and Henan have the largest water footprints, but Henan has the most significant increase, growing by 52.8% compared to 2003. Agricultural water demand (78.6~83.6%) accounts for the largest proportion of water footprint, while the ecological water demand has the smallest proportion in the entire water footprint composition. This indicates that various water uses in the NCP are mainly for agricultural water use, and there is great potential for agricultural water conservation.
- (2)
- The carbon footprint of the provinces in the NCP shows an overall upward trend, implying that with the rapid economic development, the pressure of carbon emission increases year by year, which has a great impact on the ecological environment. Among them, Shandong has the largest total carbon footprint, with the most significant increase. The emissions in 2020 (7.30 × 108 t) have reached 5.83 times the emissions in 2003 (1.25 × 108 t). The carbon footprints of coke (32.99~43.34%) and crude oil (31.55~40.29%) account for the largest proportion, and energy consumption in the NCP is dominated by coke and crude oil. With the passage of time, the proportion of natural gas is gradually increasing, and people are more and more inclined to use clean energy.
- (3)
- The total ecological footprint of the NCP is 1.95 × 108~2.24 × 108 hm2, which shows an “M”-shaped trend. The ecological footprint of arable land accounts for the largest proportion (61.5~70.1%), while water accounts for the smallest proportion (0.3~0.4%) in the composition of the whole ecological footprint, which indicates that arable land is the most important component of land resources in the NCP. Overall, the magnitude of the ecological footprint of various categories has not changed much, and the land structure of the NCP is relatively stable.
- (4)
- The coupling degree of water–carbon–ecological footprints in the NCP is high, and the interaction among water, carbon and ecological footprints is more significant. But, the coupling degree in the NCP has begun to decline continuously since 2015. Overall, the coupling coordination degree of the NCP shows a trend of increasing first, then steady, and then a continuous decline. Among the provinces in the NCP, Jiangsu has the highest mean values of coupling degree (0.851) and degree of coupling coordination (0.603) from 2003 to 2020.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Hopwood, B.; Mellor, M.; O’Brien, G. Sustainable development: Mapping different approaches. Sustain. Dev. 2005, 13, 38–52. [Google Scholar] [CrossRef]
- Gu, Y.; Wang, H.; Xu, J.; Wang, Y.; Wang, X.; Robinson, Z.P.; Li, F.; Wu, J.; Tan, J.; Zhi, X. Quantification of interlinked environmental footprints on a sustainable university campus: A nexus analysis perspective. Appl. Energy 2019, 246, 65–76. [Google Scholar] [CrossRef]
- Matuštík, J.; Kočí, V. What is a footprint? A conceptual analysis of environmental footprint indicators. J. Clean. Prod. 2021, 285, 124833. [Google Scholar] [CrossRef]
- Rees, W.E. Ecological footprints and appropriated carrying capacity: What urban economics leaves out. Environ. Urban. 1992, 4, 121–130. [Google Scholar] [CrossRef]
- Hoekstra, A.Y.; Wiedmann, T.O. Humanity’s unsustainable environmental footprint. Science 2014, 344, 1114–1117. [Google Scholar] [CrossRef]
- Tuoku, L.; Men, B. Evaluation of water resource use efficiency in Beijing-Tianjin-Hebei based on three-dimensional water ecological footprint. Ecol. Indic. 2023, 154, 110884. [Google Scholar] [CrossRef]
- Zambrano-Monserrate, M.A.; Ruano, M.A.; Ormeño-Candelario, V.; Sanchez-Loor, D.A. Global ecological footprint and spatial dependence between countries. J. Environ. Manag. 2020, 272, 111069. [Google Scholar] [CrossRef]
- Wackernagel, M.; Monfreda, C.; Schulz, N.B.; Erb, K.H.; Haberl, H.; Krausmann, F. Calculating national and global ecological footprint time series: Resolving conceptual challenges. Land Use Policy 2004, 21, 271–278. [Google Scholar] [CrossRef]
- Jaibumrung, K.; Nilsalab, P.; Gheewala, S.H.; Musikavong, C. Ecological footprint, water scarcity footprint, and benefit to cost ratio analysis towards sustainable rice production in Thailand. Sustain. Prod. Consum. 2023, 39, 79–92. [Google Scholar] [CrossRef]
- Lee, Y.J.; Chai, L.; Wu, P.S. Taiwan’s ecological footprint and overshoot day. Sci. Rep. 2021, 11, 15068. [Google Scholar] [CrossRef] [PubMed]
- Gu, X.; Wang, Q.; Liu, J.; Li, G. A new method for calculating the ecological footprint of cities based on the “national hectare”. J. Northeast. Univ. 2005, 4, 295–298. [Google Scholar]
- Li, Z.; Xue, Y. Comparison of county ecological footprint assessment based on global hectares and national hectares. Ecol. Econ. 2019, 10, 185–190. [Google Scholar]
- Zhang, R.; Li, P.; Xu, L. Evaluation and analysis of ecological security based on the improved three-dimensional ecological footprint in Shaanxi Province, China. Ecol. Indic. 2022, 144, 109483. [Google Scholar] [CrossRef]
- Li, P.; Zhang, R.; Wei, H.; Xu, L. Assessment of physical quantity and value of natural capital in China since the 21st century based on a modified ecological footprint model. Sci. Total Environ. 2022, 806, 150676. [Google Scholar] [CrossRef]
- Mancini, M.S.; Galli, A.; Niccolucci, V.; Lin, D.; Bastianoni, S.; Wackernagel, M.; Marchettini, N. Ecological footprint: Refining the carbon footprint calculation. Ecol. Indic. 2016, 61, 390–403. [Google Scholar] [CrossRef]
- Fang, K.; Heijungs, R. Investigating the inventory and characterization aspects of footprinting methods: Lessons for the classification and integration of footprints. J. Clean. Prod. 2015, 108, 1028–1036. [Google Scholar] [CrossRef]
- Fu, W.; Luo, M.; Chen, J.; Udimal, T.B. Carbon footprint and carbon carrying capacity of vegetation in ecologically fragile areas: A case study of Yunnan. Phys. Chem. Earth Parts A/B/C 2020, 120, 102904. [Google Scholar] [CrossRef]
- Dong, H.; Feng, Z.; Yang, Y.; Li, P.; You, Z. Dynamic assessment of ecological sustainability and the associated driving factors in Tibet and its cities. Sci. Total Environ. 2021, 759, 143552. [Google Scholar] [CrossRef]
- Huo, Y.; Mi, G.; Zhu, M.; Chen, S.; Li, J.; Hao, Z.; Cai, D.; Zhang, F. Carbon footprint of farming practices in farmland ecosystems on the North and Northeast China plains. J. Environ. Manag. 2024, 354, 120378. [Google Scholar] [CrossRef] [PubMed]
- Ay, H. Virtual water trade: A quantification of virtual water flows between nations in relation to international crop trade. Proc. Int. Expert Meet. Virtual Water Trade 2003, 12, 25–47. [Google Scholar]
- Liu, J.; Gao, W.; Zhang, Y.; Guo, F.; Peng, W. Overestimation of water pressure by traditional water footprint: Method revision and application. Ecol. Indic. 2024, 165, 112225. [Google Scholar] [CrossRef]
- Sturla, G.; Ciulla, L.; Rocchi, B. Estimating the global production and consumption-based water footprint of a regional economy. Sustain. Prod. Consum. 2024, 44, 208–220. [Google Scholar] [CrossRef]
- Li, N.; Zhang, R.; Shu, H.; He, A.; Zhang, X.; Wang, X. Study on the sustainable utilisation of water resources in semi-arid megacities of China based on water footprints. Water Supply 2024, 24, ws2024158. [Google Scholar] [CrossRef]
- Galli, A.; Wiedmann, T.; Ercin, E.; Knoblauch, D.; Ewing, B.; Giljum, S. Integrating ecological, carbon and water footprint into a “footprint family” of indicators: Definition and role in tracking human pressure on the planet. Ecol. Indic. 2012, 16, 100–112. [Google Scholar] [CrossRef]
- Nawab, A.; Liu, G.; Meng, F.; Hao, Y.; Zhang, Y.; Hu, Y.; Casazza, M. Exploring urban energy-water nexus embodied in domestic and international trade: A case of Shanghai. J. Clean. Prod. 2019, 223, 522–535. [Google Scholar] [CrossRef]
- Liu, Z.; Huang, Q.; He, C.; Wang, C.; Wang, Y.; Li, K. Water-energy nexus within urban agglomeration: An assessment framework combining the multiregional input-output model, virtual water, and embodied energy. Resour. Conserv. Recycl. 2021, 164, 105113. [Google Scholar] [CrossRef]
- Zhang, K.; Lu, H.; Tian, P.; Guan, Y.; Kang, Y.; He, L.; Fan, X. Analysis of the relationship between water and energy in China based on a multi-regional input-output method. J. Environ. Manag. 2022, 309, 114680. [Google Scholar] [CrossRef]
- Saqib, N. Nexus between the renewable and nonrenewable energy consumption and carbon footprints: Evidence from Asian emerging economies. Environ. Sci. Pollut. Res. 2022, 29, 58326–58340. [Google Scholar] [CrossRef]
- Nathaniel, S.P. Ecological footprint, energy use, trade, and urbanization linkage in Indonesia. GeoJournal 2021, 86, 2057–2070. [Google Scholar] [CrossRef]
- Steen-Olsen, K.; Weinzettel, J.; Cranston, G.; Ercin, A.E.; Hertwich, E.G. Carbon, land, and water footprint accounts for the European Union: Consumption, production, and displacements through international trade. Environ. Sci. Technol. 2012, 46, 10883–10891. [Google Scholar] [CrossRef]
- Ridoutt, B.G.; Page, G.; Opie, K.; Huang, J.; Bellotti, W. Carbon, water and land use footprints of beef cattle production systems in southern Australia. J. Clean. Prod. 2014, 73, 24–30. [Google Scholar] [CrossRef]
- Čuček, L.; Klemeš, J.J.; Kravanja, Z. Objective dimensionality reduction method within multi-objective optimisation considering total footprints. J. Clean. Prod. 2014, 71, 75–86. [Google Scholar] [CrossRef]
- Xiao, D.; Li Liu, D.; Wang, B.; Feng, P.; Bai, H.; Tang, J. Climate change impact on yields and water use of wheat and maize in the North China Plain under future climate change scenarios. Agric. Water Manag. 2020, 238, 106238. [Google Scholar] [CrossRef]
- Zhang, C.; Duan, Q.; Yeh, P.J.; Pan, Y.; Gong, H.; Gong, W.; Di, Z.; Lei, X.; Liao, W.; Huang, Z.; et al. The effectiveness of the South-to-North Water Diversion Middle Route Project on water delivery and groundwater recovery in North China Plain. Water Resour. Res. 2020, 56, e2019WR026759. [Google Scholar] [CrossRef]
- Hoekstra, A.Y.; Chapagain, A.K. Water footprints of nations: Water use by people as a function of their consumption pattern. In Integrated Assessment of Water Resources and Global Change: A North-South Analysis; Springer: Berlin/Heidelberg, Germany, 2007; pp. 35–48. [Google Scholar]
- Liu, M.; Li, W.; Zahng, D.; Su, N. The calculation of equivalence factor for ecological footprints in China: A methodological note. Front. Environ. Sci. Eng. 2015, 9, 1015–1024. [Google Scholar] [CrossRef]
- Jing, P.; Sheng, J.; Hu, T.; Mahmoud, A.; Guo, L.; Liu, Y.; Wu, Y. Spatiotemporal evolution of sustainable utilization of water resources in the Yangtze River Economic Belt based on an integrated water ecological footprint model. J. Clean. Prod. 2022, 358, 132035. [Google Scholar] [CrossRef]
- Zhang, J.; Duan, Y.; Zhou, S.; Huang, Y. Evaluation of the Effectiveness of Water Ecological Restoration Based on the Relationship between the Supply and Demand of Ecological Products—A Case Study of the Yellow River Delta. Land 2023, 12, 2093. [Google Scholar] [CrossRef]
- IPCC. IPCC Guidelines for National Greenhouse Gas Inventories; IPCC: Tokyo, Japan, 2006. [Google Scholar]
- Ogle, S.M.; Kurz, W.A.; Green, C.; Brandon, A.; Baldock, J.; Domke, G.; Herold, M.; Bernoux, M.; Chirinda, N.; de Ligt, R.; et al. Refinement to the 2006 IPCC guidelines for national greenhouse gas inventories. In Intergovernmental Panel on Climate Change; IPCC: Geneva, Switzerland, 2019. [Google Scholar]
- Xi, J.; Gong, H.; Zhang, Y.; Dai, X.; Chen, L. The evaluation of GHG emissions from Shanghai municipal wastewater treatment plants based on IPCC and operational data integrated methods (ODIM). Sci. Total Environ. 2021, 797, 148967. [Google Scholar] [CrossRef] [PubMed]
- Azarkamand, S.; Ferré, G.; Darbra, R.M. Calculating the Carbon Footprint in ports by using a standardized tool. Sci. Total Environ. 2020, 734, 139407. [Google Scholar] [CrossRef]
- Zhang, R.; Li, P.; Xu, L.; Zhong, S. Reconciling ecological footprint and ecosystem services in natural capital accounting: Applying a novel framework to the Silk Road Economic Belt in China. J. Environ. Manag. 2023, 330, 117115. [Google Scholar] [CrossRef] [PubMed]
- Muhirwa, F.; Shen, L.; Elshkaki, A.; Zhong, S.; Hu, S.; Hirwa, H.; Chiaka, J.C.; Umarishavu, F.; Mulinga, N. Ecological balance emerges in implementing the water-energy-food security nexus in well-developed countries in Africa. Sci. Total Environ. 2022, 833, 154999. [Google Scholar] [CrossRef]
- Qi, Y.; Farnoosh, A.; Lin, L.; Liu, H. Coupling coordination analysis of China’s provincial water-energy-food nexus. Environ. Sci. Pollut. Res. 2021, 29, 23303–23313. [Google Scholar] [CrossRef] [PubMed]
- Shu, J.; Bai, Y.; Chen, Q.; Weng, C.; Zhang, F. Dynamic simulation of the water-land-food nexus for the sustainable agricultural development in the North China Plain. Sci. Total Environ. 2024, 912, 168771. [Google Scholar] [CrossRef] [PubMed]
- Dai, J.; Khan, Y.A. Ecological environment pressure state and response system for coupling coordinate development: An application on china data. Environ. Sci. Pollut. Res. 2023, 30, 25682–25690. [Google Scholar] [CrossRef] [PubMed]
- Li, W.; Liang, Y.; Liu, L.; He, Q.; Huang, J.; Yin, Z. Spatio-temporal impacts of land use change on water-energy-food nexus carbon emissions in China, 2011–2020. Environ. Impact Assess. Rev. 2024, 105, 107436. [Google Scholar] [CrossRef]
- Zhao, J.; Zhang, X.; Yang, Y.; Zang, H.; Yan, P.; Meki, M.N.; Doro, L.; Sui, P.; Jeong, J.; Zeng, Z. Alternative cropping systems for groundwater irrigation sustainability in the North China Plain. Agric. Water Manag. 2021, 250, 106867. [Google Scholar] [CrossRef]
Name of Energy Source | Average Low-Level Calorific Value (TJ/Gg) | Carbon Content Per Unit Calorific Value (tC/TJ) | Carbon Oxidation Rate (%) |
---|---|---|---|
Coke | 28.470 | 29.42 | 93 |
Crude Oil | 41.868 | 20.08 | 98 |
Petrol | 43.124 | 18.90 | 98 |
Gasoline | 43.124 | 19.60 | 98 |
Diesel Oil | 42.705 | 20.20 | 98 |
Bunker Fuel | 41.868 | 21.10 | 98 |
Liquefied Petroleum Gas | 50.242 | 17.20 | 98 |
Petroleum | 38.979 | 15.32 | 99 |
Biologically Productive Land Types | Account Product Type | |
---|---|---|
Arable land | Agricultural production | Grain, cotton, oilseeds, vegetables, tobacco, hemp, pork, poultry and eggs |
Grassland | Livestock products | Beef, lamb, dairy |
Woodland | Forest products | Fruits, tea, oilseeds |
Watershed | Fishery products | Fishery products |
Interval of Coupling C Values | Degree of Coupling | Interval of D-Values for Coupling Coordination | Degree of Coupling Coordination |
---|---|---|---|
(0.0~0.3) | Low-level coupling | (0.0~0.1) | Extreme disorder |
[0.1~0.2) | Severe disorder | ||
[0.2~0.3) | Moderate disorder | ||
[0.3~0.5) | Antagonistic period | [0.3~0.4) | Mild disorder |
[0.4~0.5) | On the verge of a disorder | ||
[0.5~0.8) | Break-in period | [0.5~0.6) | Sue for coordination |
[0.6~0.7) | Primary coordination | ||
[0.7~0.8) | Mid-level coordination | ||
[0.8~1.0) | High-level coupling | [0.8~0.9) | Good coordination |
[0.9~1.0) | High-quality coordination |
Year | Interval of Coupling C Values | Degree of Coupling | Interval of D-Values for Coupling Coordination | Degree of Coupling | Degree of Coupling Coordination |
---|---|---|---|---|---|
2003 | 0.336 | 0.670 | 0.475 | Antagonistic period | On the verge of a disorder |
2004 | 0.950 | 0.674 | 0.800 | High-level coupling | Good coordination |
2005 | 0.996 | 0.735 | 0.856 | High-level coupling | Good coordination |
2006 | 0.982 | 0.790 | 0.881 | High-level coupling | Good coordination |
2007 | 1.000 | 0.699 | 0.836 | High-level coupling | Good coordination |
2008 | 0.983 | 0.713 | 0.837 | High-level coupling | Good coordination |
2009 | 0.955 | 0.647 | 0.786 | High-level coupling | Mid-level coordination |
2010 | 0.936 | 0.585 | 0.740 | High-level coupling | Mid-level coordination |
2011 | 0.894 | 0.507 | 0.673 | High-level coupling | Primary coordination |
2012 | 0.821 | 0.504 | 0.643 | High-level coupling | Primary coordination |
2013 | 0.741 | 0.429 | 0.564 | Break-in period | Sue for coordination |
2014 | 0.621 | 0.437 | 0.521 | Break-in period | Sue for coordination |
2015 | 0.512 | 0.376 | 0.439 | Break-in period | On the verge of a disorder |
2016 | 0.793 | 0.155 | 0.351 | Break-in period | Mild disorder |
2017 | 0.650 | 0.120 | 0.279 | Break-in period | Moderate disorder |
2018 | 0.632 | 0.109 | 0.262 | Break-in period | Moderate disorder |
2019 | 0.433 | 0.023 | 0.101 | Antagonistic period | Severe disorder |
2020 | 0.159 | 0.027 | 0.065 | Low-level coupling | Extreme disorder |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 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 (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Lyu, K.; Tian, J.; Zheng, J.; Zhang, C.; Yu, L. Evaluation of Water–Carbon–Ecological Footprint and Its Spatial–Temporal Changes in the North China Plain. Land 2024, 13, 1327. https://doi.org/10.3390/land13081327
Lyu K, Tian J, Zheng J, Zhang C, Yu L. Evaluation of Water–Carbon–Ecological Footprint and Its Spatial–Temporal Changes in the North China Plain. Land. 2024; 13(8):1327. https://doi.org/10.3390/land13081327
Chicago/Turabian StyleLyu, Keyi, Jin Tian, Jiayu Zheng, Cuiling Zhang, and Ling Yu. 2024. "Evaluation of Water–Carbon–Ecological Footprint and Its Spatial–Temporal Changes in the North China Plain" Land 13, no. 8: 1327. https://doi.org/10.3390/land13081327