Examining spatial carbon metabolism: Features, future simulation, and land-based mitigation
Introduction
Three-quarters of the total global carbon emissions have been found to occur in urban areas (Intergovernmental Panel on Climate Change (IPCC), 2007), which only occupy 2% share of the world land area. Hence, cities are rethinking their responsibility for carbon mitigation (Grimm et al., 2008). As shown in previous studies, land use and cover change (LUCC) is considered as a primary driver of carbon storage loss. For example, land turned into cultivated land and forest lands and grasslands with high carbon storage densities have been converted into urban built-up lands with low carbon storage densities (Wu et al., 2016; De Carvalho and Szlafsztein, 2019). The acceleration of urbanization has inevitably changed the land cover from natural vegetation to urban patterns with complicated man-made surfaces (Zhao et al., 2019; Su et al., 2020). These variations significantly influence the carbon emission and sequestration cycles in the biosphere, a process that is defined as carbon metabolism in urban metabolism studies (Hutyra et al., 2011; Cui, 2018). In China, urbanization, rapid population migration, and land use transitions have made carbon emissions increase dramatically, leading to a challenge for carbon mitigation (Wang et al., 2018a). Thus, an understanding of the spatial carbon distribution and patterns can provide insights for future mitigation policies from a land-based perspective.
To explore the carbon emission and sequestration changes, the development of methods for carbon assessments has been focused on for several decades. The International Council for Local Environmental Initiatives (ICLEI) first developed a carbon accounting method to calculate the emissions generated by energy use in the 1990s (ICLEI, 1993). In 2006, the IPPC (International Panel on Climate Change) established carbon emission accounting guidelines, which provided empirical coefficients to estimate fuel carbon emissions (IPCC, 2006). With the advantage of energy consumption data availability, energy-related emissions have since been widely studied in urban areas (Kennedy et al., 2010; Kellett, 2013). Different from emissions, carbon sequestration is considered to be related to the land cover types in a natural system (Tao et al., 2015; Xu et al., 2016). The carbon sequestration capacity of different land cover types, usually reflected by the carbon storage density, have typically been used as constant coefficients for most carbon sequestration estimations (Long et al., 2014; Li et al., 2020). To support these sequestration assessments, remote sensing has been used to interpret land cover areas and monitor changes by scholars (BP, 2019). Thus, studies of carbon metabolism have typically been conducted as: (1) sequestration changes due to land cover changes, and (2) emission changes caused by energy consumption variations in a socioeconomic system. Although there are growing literatures on territorial carbon change and related assessment, studies referred to cities and rural settlements in developing countries were still limited, making the land-related carbon estimation in non-developed cities a challenge. (Chavez et al., 2012; Paloheimo and Salmi, 2013; Minx et al., 2013; Sohn et al., 2018; Semieniuk and Yakovenko, 2020).
Since the application of the Geographic Information System (GIS) in carbon emission/sequestration studies, research on the spatial patterns of carbon emission/sequestration dynamics has been focused on land component layouts. Some studies have examined spatial carbon sequestration features among different natural land covers to reveal the effects of land urbanization (Liu et al., 2018; Wu et al., 2018; Xia et al., 2019). By focusing on one land cover type (for example, the forest), Rittenhouse and Rissman built three scenarios including a static forest, an all forest, and a dynamic forest to examine future land cover change effects on carbon sequestration (Rittenhouse and Rissman, 2012). Future carbon sequestration simulations concerning other land cover types, such as grasslands and croplands, have also been studied using a scenario analysis (Wang et al., 2015). However, these sequestration simulations have primarily been quantitative assessments without a spatial analysis. In addition, they did not consider the interactions and competitions between multiple LUCC types, which ignores the complex interrelationships between the land components in the LUCC processes (Liu et al., 2017).
Limited by data availability, spatial carbon emission studies have typically been conducted at national or regional scales by using economic models (Cai et al., 2018). Researchers have calculated the energy-related carbon emissions of different cities and mapped their differentiation to reveal the differences between cities. Different from those large-scale studies, Zhang et al. used land areas and related empirical conversion coefficients of different land use types to calculate carbon emissions and analyzed their spatial distribution at the city level (Zhang et al., 2014). Other methods, such as the ecological network model, have also been applied to study the spatial carbon metabolism between urban land components (Xia et al., 2016). According to the network method in the study of Xia et al., land transfers between a given pair of components can produce the same relationship (mutualism) or a different relationship (such as competition) in the following period, which is then used to describe the spatial carbon emissions. Future carbon simulations have been another focus in this field to reflect possible carbon dynamics in urban areas. Limited by data availability and estimation accounting, current references have typically used regression analyses (e.g., concerning economic development or energy consumption in Xu et al., 2015) to predict future carbon quantities, rather than spatial simulations. Other studies have simulated carbon emissions by using a network model supported by highly aggregate data, and thus these studies have provided a snapshot of carbon emissions at the regional level without specific analysis of locations, activities, or people (Chen et al., 2015). Thus, spatial information of future carbon emissions is vitally important for effective mitigation from a land-based perspective (Madlener and Sunak, 2011).
To summarize, considerable improvements have been made in the field of carbon metabolism studies, a number of which are noted in Fig. 1. However, simulations of future spatial carbon metabolism that integrate both carbon emissions and sequestration changes in urban spatial layouts are needed to understand how decarbonization can work through land-based policy. To address this research target, this study aims to do the following: (1) Develop a land-based carbon metabolism accounting to assess carbon sequestration and emissions for different land use types that uses carbon metabolism as a sub-process of urban metabolism; and (2) establish future spatial carbon scenarios that couple the governmental decarbonization target with multiple-type land use simulations to locate specific areas for effective land-based mitigation.
Section snippets
The study framework
In this section, a carbon metabolism framework is developed including: (1) the land-based accounting; (2) the spatial pattern assessment; and (3) the future spatial simulation modeling. This approach can be conducted by three steps. Firstly, the land-based carbon emissions and sequestrations were estimated according to the established carbon accounting. Then, the spatial carbon metabolism pattern was investigated by using the land use transition matrix, which is supported by the remote sensing
Study area
Guangzhou is the third largest city and an important economic and cultural center of China, which can be taken as a representative of the rapid developed cities in China. Guangzhou was selected as the empirical object in this carbon metabolism study for the purpose: (1) to examine its spatial carbon metabolism feature by using the land-based carbon accounting; and (2) to testify the method put forward by this study, which can be used as a reference for other cities. This empirical study will be
Carbon metabolism pattern
The carbon emission rate in Guangzhou increased from 2313.63 × 107 kgC yr−1 in 2000 to 3392.52 × 107 kgC yr−1 in 2015, which shows a growth of 31.7% during these 15 years. Carbon sequestration decreased from 207.27 × 106 kgC yr−1 to 199.6 × 106 kgC yr−1, with a slight declining trend. Fig. 6 illustrates the metabolism pattern among the different components. Components T and U are considered responsible for the largest carbon emissions in Guangzhou. Emissions from component T accounted for
Discussion
Future carbon scenarios and their spatial dimensions are conducted to assist policy makers in reducing carbon emissions at the city level by effective and feasible land use and allocation points. For Guangzhou, the growing metropolis with its polycentric urban layout plan, an integrated concentration should be interlinked and coordinated to show where and how varied types of development should be allowed (Huang and Wang, 2016). Similarly, these spatial carbon scenarios for 2030 are helpful for
Conclusion
This study developed a methodological framework for spatial carbon metabolism assessments and future simulations. The contributions of this study were as follows: (1) developing a land-based accounting for carbon assessment using the linkage between carbon metabolism and land use type; and (2) introducing spatial simulation modeling for future carbon scenarios to render a pro-active approach for local land-based carbon mitigation. The established framework is a suitable alternative for
Author contribution statement
Xuezhu Cui: Conceptualization, Methodology, Formal analysis, Writing, and Funding acquisition; Shaoying Li: Resources, Project administration, Writing – review & editing and Funding acquisition; Feng Gao: Software, Data Curation, Visualization
Declaration of Competing Interest
We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper entitled “Examining spatial carbon metabolism: features, future simulation, and land-based mitigation”.
Acknowledgments
We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript. This work was supported by the Project of National Natural Science Foundation of China [grant numbers 71603062, 71601042]; the Project of philosophy and social sciences in Guangdong Province [grant number GD14CGL02]; the Project of Guangzhou University's 2017 training program for young top-notch personnel [grant number BJ201723]; and the Key Special Project for Introduced Talents Team of
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