Journal of Cleaner Production 426 (2023) 139147
Available online 30 September 2023 0959-6526/© 2023 Published by Elsevier Ltd.
Can forest carbon sequestration offset industrial CO 2 emissions? A case study of Hubei Province, China
Jing Cheng
a, Chunbo Huang
a,b,*, Xintao Gan
a, Changhui Peng
b, Lei Deng
c,daHubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China
bDepartment of Biological Sciences, University of Qu´ebec at Montreal, Montreal, QC, H3C 3P8, Canada
cState Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, Shaanxi, China
dInstitute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi, China
A R T I C L E I N F O Handling Editor: Jin-Kuk Kim Keywords:
Carbon neutrality Industrial carbon emissions Forest carbon sequestration Offseting effect
A B S T R A C T
Carbon peaking and carbon neutrality goals are proposed by the Chinese government, which aims to reach the highest level of CO2 emissions by 2030 and achieve carbon neutrality by 2060. It is of great significance to accelerate green production and lead high-quality development. Energy carbon emissions account for 88% of total CO2 emissions, while forest carbon sequestration account for 80% of China’s terrestrial ecosystems. In this paper, we analyzed the spatial and temporal evolutions of industrial energy consumption carbon emissions and forest carbon sequestration of Hubei Province, then demonstrated the offsetting effect of forest carbon seques- tration on the industrial CO2 emissions. Our results documented energy carbon emissions and forest carbon sequestration in Hubei Province both increased from 2000 to 2020, with the growth rates of 4.9423 Mt/yr and 0.28015 Mt/yr. Forest carbon sequestration could offset energy carbon emissions before 2005, while the off- setting effect was weak due to the continuous increase in industrial energy consumption after 2005. Significant spatial heterogeneity was observed in both energy carbon emissions and forest carbon sequestration. Wuhan had the biggest carbon emission with annual average carbon emission of about 20.046 Mt, while Enshi had the biggest carbon sequestration with annual average forest carbon sequestration of about 14.411 Mt. Spatial autocorrelation between energy carbon emissions and forest carbon sequestration was significant in Hubei Province in the past two decades. Our findings provided evidence that local-scale forest carbon sequestration could offset carbon emissions caused by industrial energy consumption at a certain extent, helping to draw up the scientific energy-saving and emissions-reducing measures.
1. Introduction
In September 2020, the Chinese government proposed the carbon peaking and carbon neutrality goals, aiming to reach the peak of CO2
emissions by 2030 and achieve the carbon neutrality by 2060 (Hao et al., 2022). Energy consumption, particularly from fossil fuels, is the primary contributor to carbon emissions (Waheed et al., 2019). Some studies (such as Zhou et al., 2021, Zhang et al., 2022; Hu et al., 2020) have explored carbon emissions, and analyzed the amount of carbon emis- sions in various regions. Meanwhile, some studies documented the effect of regional development patterns on carbon emission reduction (Zheng et al., 2019), the efficacy of policies aimed at reducing carbon emissions
(Zhang et al., 2020), and the urban forest offsetting effect of carbon emissions (Zhao et al., 2010). However, few studies have explored the carbon footprint of diverse activities within a particular region and the feasibility of forest carbon offsets as a means of achieving net-zero emissions.
China emitted 9.899 billion tons of CO2, with 88% coming from energy consumption, which is the primary cause of the country’s high carbon footprint (http://www.nea.gov.cn/). Relevant empirical evi- dence (such as Kais and Sami, 2016) suggests that there is an inverted U-shaped relationship between CO2 emissions and GDP per capita, and carbon emission intensity will first increase and then decrease with economic growth, eventually leading to high-quality development. The
* Corresponding author.Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China Uni- versity of Geosciences, Wuhan, 430074, China.
E-mail addresses: [email protected] (J. Cheng), [email protected] (C. Huang), [email protected] (X. Gan), [email protected] (C. Peng), [email protected] (L. Deng).
Contents lists available at ScienceDirect
Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
https://doi.org/10.1016/j.jclepro.2023.139147
Received 21 July 2023; Received in revised form 11 September 2023; Accepted 29 September 2023
Journal of Cleaner Production 426 (2023) 139147 burning fossil fuels led to strong absorption of long-wave radiation,
resulting in the greenhouse effect, glacial melting, climate anomalies, and other phenomena, posing a threat to human survival (World Meteorological Organization | (wmo.int)). Meanwhile, carbon emissions concentrate on five key areas: electricity, construction, industrial pro- duction, transportation, and agriculture (Waheed et al., 2019). Among them, industrial production accounted for 66.7% of total annual energy consumption, and was a major contributor to carbon emissions in 2020 (Fang et al., 2022). Despite the national proposal to achieve carbon peaking in 2030, carbon emissions will continue to increase in the coming years. Therefore, it is essential to reveal the spatio-temporal evolution of industrial energy consumption CO2 emissions for control- ling the future carbon emission and reaching the carbon peaking.
As the largest terrestrial ecosystem participating in the region as well as the global carbon cycle, forest ecosystems dominate the planet, with increasing biomass density of forest vegetation since 1990, helping balance carbon emissions and alleviate climate change (Wang et al., 2020; Yu et al., 2021). Green plants can absorb large amounts of CO2, which can maintain carbon balance, mitigate the greenhouse effect and provide material and energy for humans (Tkemaladze et al., 2016). Qiu et al. (2020) have predicted that China’s forest vegetation area will in- crease from 2188.489 km2 to 2971.068 km2 from 2003 to 2050, with carbon storage and concentration exhibited an increasing trend, indi- cating an improvement in forest quality. Forest resources possess self-regenerative characteristics that provide ecological, economic, and sustainable benefits (Khaine et al., 2015). By stabilizing CO2 through carbon sequestration mechanisms, forests can sustainably sequester carbon emission caused by energy consumption, which is essential for regional sustainable development (Martire et al., 2015). As the primary terrestrial vegetation, forest carbon sequestration accounts for over 80%
of China’s terrestrial ecosystem carbon sequestration and sequester 14.1% of energy carbon emissions (Hao et al., 2022). Therefore, assessing energy consumption carbon emissions and conducting forest carbon sequestration are crucial for understanding carbon sources and sequestration hotspots at the regional scale, which could provide some recommendations for forest management and regional development under the carbon peaking and carbon neutrality goals.
In 2013, the Chinese government elevated the Yangtze River Eco- nomic Belt to a national development strategy to coordinate economic growth with environmental protection and economic development (Chen et al., 2017). The Yangtze River Economic Belt connects the eastern, central and western parts of China, breaking trade barriers, utilizing the Yangtze River system to facilitate economic expansion (Han et al., 2019). As the center province of the Yangtze River Economic Belt and the only province with over 1000 km on the Yangtze River mainline, Hubei Province holds an outstanding position and has a unique re- sponsibility in the economic belt development of the Yangtze River.
Achieving carbon peaking and carbon neutrality goals is crucial for the high-quality development of the Yangtze River Economic Belt and the entire country (Wang et al., 2022c). However, there is a significant spatial mismatch between industrial carbon emissions and forest carbon sequestration in Hubei Province (Yang et al., 2023). Forests are mainly distributed in the west and north regions of Hubei, whereas the main industrial cities, including Huangshi, Ezhou and Wuhan, are situated in the east of Hubei the main industrial cities, which have traditional iron and steel smelting and metallurgy. Therefore, it is necessary to analyse the spatial and temporal evolutions of industrial carbon emissions and forest carbon sequestration of Hubei Province, which could to under- stand the offsetting effect of forest carbon sequestration on industrial energy carbon emissions and learn the causes and consequences of carbon emission gap.
To fill this gap, we firstly calculated the energy carbon emissions and forest carbon sequestration in Hubei Province, and then analyzed the offsetting effect of forest carbon sequestration on energy carbon emis- sions to validate whether the forest carbon offsetting is enough for a net zero. The specific objectives are: (1) Revealing the spatio-temporal
evolution of industrial carbon emissions in Hubei Province over the past 20 years; (2) Demonstrating the benefits of forest carbon seques- tration in compensating industrial carbon emissions and analyzing the impact of the carbon emission gap on achieving carbon neutrality; (3) Proposing recommendations for energy conservation and vegetation management. This study is further strengthened by examining the regional carbon emission and sequestration dynamics in the context of energy efficiency and carbon efficiency evaluation, with the aim of guiding the carbon trading activities and regional carbon emission reduction planning in Hubei Province. Our findings could provide evi- dence that local-scale forest carbon sequestration could offset carbon emissions caused by industrial energy consumption at a certain extent, helping to draw up the scientific energy-saving and emissions-reducing measures.
2. Methods 2.1. The study area
Hubei Province is selected as the study area, located in central China between N29◦01
′
53″
–33◦6′
47″
and E108◦21′
42″
–116◦07′
50″
, spanning approximately 740 km from east to west and 470 km from north to south (Fig. 1). Hubei is in central China and shares borders with Henan to the north, Jiangxi and Hunan to the south, Chongqing to the west, and Anhui to the east, which covers a total area of 185,900 km2, including 12 prefecture-level administrative cities (such as Wuhan, Huangshi, and Xiangyang), 1 autonomous prefecture (Enshi), and 4 municipalities (Xiantao, Qianjiang, Tianmen, and Shennongjia). The topography of Hubei is diverse, with high terrain in the east, west, and north, low in the south, and open to the south in an incomplete manner. The western region is mainly mountainous, the eastern region is hilly, and the south- central region is plain. Hubei Province has a subtropical monsoon climate, with abundant rivers, flora, fauna, and mineral resources.2.2. Technology roadmap
This study mainly includes four parts (Fig. 2). First, we collected the study data from 2000 to 2020 in 103 counties of Hubei Province. Sec- ond, trend analysis and spatial analysis were used to analyze the spatial and temporal patterns of industrial energy consumption carbon emis- sion and forest carbon sequestration. Third, the offsetting effect of forest carbon sequestration on energy carbon emissions was calculated and analyzed to document how does Hubei Province change from carbon sequestration to carbon source. Finally, suggestions on forest manage- ment and achieving carbon peaking and carbon were proposed to help achieving carbon neutrality goal.
2.3. Data sources and processing
The study data include statistic data, remote sensing data and vector data. Industrial energy consumption data with 103 counties in Hubei Province are collected from the Hubei County Regional Economic and Social Development Statistical Bulletin, the County Statistical Yearbook on energy consumption, and Chinese statistic yearbooks. NPP data are collected from MOD17A3HGF product data (https://earthexplorer.usgs.
gov/) from 2000 to 2020. Ecosystem maps for five periods (2000, 2005, 2010, 2015 and 2020) are acquired from the Resource and Environ- mental Science and Data Center (https://www.resdc.cn/). Meanwhile, 103 county vector and 17 city vector data in Hubei Province are collected to calculate their energy carbon emissions and forest carbon sequestration. Moreover, supporting data such as GDP, population and industrial structure of Hubei Province are obtained from Hubei Statis- tical Yearbook, China City Statistical Yearbook and “City GDP Data Sheet” to explore the relationship between economic development and forest protection.
J. Cheng et al.
2.3.1. Assessing energy carbon emission
According to the IPCC (Table 1), the energy carbon emissions are calculated by multiplying the collected energy consumption data with the corresponding energy carbon emission factors for each type of en- ergy and then converting the basic carbon units (https://www.ipcc.ch/).
Energy carbon emissions during 2000–2020 relies on energy consump- tion data primarily obtained from the statistical yearbook of 103 counties in Hubei Province on an annual basis, and the carbon emission is as the following equation.
Ec=∑
i
Fi×EFi×10−6 (1)
where Ec is the industrial energy consumption carbon emission at the county level (Mt). Fi is the total energy consumption of the i-th fuel type (104 tce). EFi is the effective carbon emission factor of i-th fuel type. The specific emission factors of the main fossil fuel utilized in Hubei Prov- ince are shown in Table 2.
2.3.2. Assessing forest carbon sequestration
Forest NPP data are generated by integrating the NPP data and
ecosystem maps. Ecosystem map is used to identify the forest location, and we have extracted the forest NPP data in Hubei Province from the MOD17A3HGF product. Although annual ecosystem maps are not available, ecosystems do not change drastically in a short duration (Teng et al., 2019; Huang et al., 2020). Forest location for different periods are estimated on the basis of ecosystem data in 2000 (2000–2002), 2005 (2003–2007), 2010 (2008–2012), 2015 (2013–2017) and 2020 (2018–2020).
Annual forest carbon sequestration for each county is calculated by the mean forest NPP and forest area in this county, and is as the following equation.
Fc=
∑
i
NPPj×Aj×10−12 (2)
where Fc is the forest carbon sequestration at the county level (Mt). NPPj
is the mean forest NPP of the j-th county (gC/m2). Aj is the forest area of the j-th county (m2).
Fig. 1. Location of the Yangtze River Basin in China (a), and location of Hubei Province in Yangtze River Basin, and the study area geographical condition (c).
Journal of Cleaner Production 426 (2023) 139147
2.3.3. Offsetting effect of forest carbon sequestration on energy carbon emission
Difference of forest carbon sequestration and energy carbon emission is used to assess the offsetting effect of each county, and the effect value is calculated by the following equation.
Ef=Fc− Ec (3)
where Ef is the offsetting effect value of forest carbon sequestration on energy carbon emission at the county level (Mt Carbon). Ec is the in-
dustrial energy carbon emission of this county (Mt Carbon), while Fc is the forest carbon sequestration of this county (Mt Carbon).
2.4. Analysis methods 2.4.1. Trend analysis
The trend analysis is conducted to comprehensively reveal the changing trends of energy carbon emissions and forest carbon seques- tration in Hubei Province over the past 20 years. A least-square linear regression model is applied to energy carbon emission, forest carbon sequestration and offsetting effect value for each county, and we have used the modelled slope to describe the changing trend for these vari- ables (Huang et al., 2018).
y=ax+b (4)
Fig. 2.The technology roadmap of this study.
Table 1
List of abbreviations in research and variables in the computational model.
Abbreviation
and variable Full name Definition or explanation IPCC Intergovernmental Panel
on Climate Change The United Nations body for assessing the science related to climate change, preparing comprehensive Assessment Reports, producing Special Reports on topics, and providing guidelines for the preparation of greenhouse gas inventories.
CO2 Carbon dioxide A carbon-oxygen compound that
is a common greenhouse gas NPP Net Primary Productivity NPP is the net carbon gain by
plants, and is generally measured at the ecosystem scale over relatively long-time intervals, such as a year (g Carbon m−2 yr−1).
Mt Million tons of carbon Units of carbon emission or sequestration
tC/TJ Tons of carbon/1012 J Carbon emission factor, the amount of carbon released per TJ tC/104tce Tons of carbon/104 tons of
standard coal equivalent Units of effective carbon emission factors, the amount of carbon per 104 tons of standard coal
tC/MWh Tons of carbon/106 Watt-
Hour Units of effective carbon
emission factors, the amount of carbon per megawatt hour
Table 2
Carbon emission factor by the main fossil fuel type.
Fossil fuel Carbon emission
factor a/(tC/TJ) Contribution
rate/% Effective carbon emission factor b/(tC/
104tce)
Coal 25.4 100 7444.1
Coke 29.5 100 8645.8
Gas 18.9 100 5539.1
Diesel 20.2 100 5920.1
Fuel 21.1 100 6183.9
Liquefied Petroleum Gas
17.2 100 5040.9
Heat 10112.8 c
Electricity 0.2392 tC/MWh c
*Note.
a Carbon emission factor of these fossil fuels are collected from the website of cbcsd.org.cn.
b Effective carbon emission factor derived from the combination of carbon emission factor and standard coal conversion factor; various energy sources converted to standard coal factor reference (samr.gov.cn).
cEffective carbon emission factor for heat and power refer to Zhou et al.
(2013).
J. Cheng et al.
where y is the energy carbon emission, forest carbon sequestration or offsetting effect value (Mt) of each county or city, x is the time (yr), a is the modelled slope (Mt/yr), and b represents the intercept of the regression model.
This analysis returns the slope of the linear regression line that passes through the known_y’s and known_x’s data points. A large slope in- dicates a significant trend in energy emissions data/forest carbon sequestration data in the region. Conversely, a small slope represents that the energy emissions data/forest carbon sequestration data of the region are not significant and tend to be flat.
2.4.2. Spatial analysis
Hotspot analysis is performed for the multi-year average energy carbon emission and forest carbon sequestration in Hubei Province at the county level. It is a commonly used method for measuring spatial relationships, which can not only indicate the existence of clustering phenomena, but also measure the intensity of spatial units with clus- tering phenomena relative to the entire study area (Cheng et al., 2018).
Z-score and p-value are used to measure the spatial clustering re- lationships of the study elements. Hot spots reflect high values pre- senting spatial clustering phenomenon with high z-score and small p-value. In contrast, cold spots reflect low values of spatial clustering with low negative z-scores and small p-values. Higher (or lower) z-scores indicate stronger clustering, and z-scores close to zero indicate no sig- nificant spatial clustering, an insignificant clustering.
3. Results
3.1. Spatio-temporal evolution of energy carbon emissions
Over the past two decades, there has been a consistent upsurge of energy carbon emissions of Hubei Province, with an increasing rate of 3.7873 Mt/yr (Fig. 3). There was a steady progression from 36.89 Mt in 2001 to 92.26 Mt in 2012, representing an average growth rate of 4.9423 Mt/yr. Whereas the carbon emissions have fluctuated between
86.06 Mt and 123.24 Mt during 2013–2020.
There was a notable spatial correlation in carbon emissions associ- ated with energy consumption within Hubei Province. The central and eastern regions tended to have higher emissions, while the western re- gion had lower emissions (Fig. 4, a). Wuhan had the highest energy carbon emissions, with a multi-year average carbon emission of 20.046 Mt, followed by Xiangyang with a multi-year average carbon emission of 7.090 Mt. Shennongjia had the lowest carbon emission, with a multi- year average carbon emission of approximately 0.106 Mt.
The hotspot analysis results of multi-year average energy carbon emission of Hubei Province showed significant spatial aggregation of energy carbon emissions in the past two decades (Fig. 4, c). Additionally, these cold and hot spots have remained in the same spatial locations.
Notably, energy carbon emission hotspots were concentrated in south- east region of Hubei, including Wuhan, Xiaogan and Ezhou, demon- strating significant spatial clustering. Conversely, the cold spot areas of carbon emissions were concentrated in Enshi in the southwest, while other regions did not exhibit significant clustering effects.
Overall, the increasing trend of energy carbon emissions was more prominent in the eastern Hubei (Fig. 4, b), particularly in Wuhan, Ezhou, Xiaogan and the northwestern Huangshi in the south region. Conversely, the energy carbon emissions in Enshi, Shennongjia and Shiyan were more moderate, and the growth trend was not significant.
3.2. Spatio-temporal evolution patterns of forest carbon sequestration Forest carbon sequestration of Hubei Province increased from 61.70 Mt in 2000 to 67.86 Mt in 2020, with a growth rate of 0.28015 Mt/yr (Fig. 5). Although the R2 of the least-square linear regression model was lower, the p-value of the t statistic of the modelled slope was less than 0.01, indicating the increasing trend was statistically significant.
The multi-year average forest carbon sequestration in Hubei exhibited significant spatial heterogeneity (Fig. 6, a). Enshi had the highest forest carbon sequestration with a multi-year average carbon sequestration of 14.411 Mt, followed by Shiyan (10.985 Mt) and
Fig. 3.Temporal variations of industrial CO2 emissions in Hubei from 2000 to 2020. Note: Blue point is annual CO2 emission of the Hubei province. Red dotted line indicates the change trend of annual CO2 emission, and the gray region is the 95% confidence intervals of the linear model. The changing trend is described by the modelled slope (Mt/yr) of the least-square linear regression model, and the t statistic to test the significance of the changing trend, which is documented by the p- value. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Journal of Cleaner Production 426 (2023) 139147
Yichang (10.642 Mt). Meanwhile, Xiantao had the lowest forest carbon sequestration, with multi-year average carbon sequestration of 0.007 Mt. In general, there were differences in the level of energy carbon emissions and carbon sequestration in different regions. Besides, carbon sources and carbon sink had opposite spatial distribution patterns (Figs. 4c and 6c), which is consistent with Pan et al. (2022).
Hotspot analysis showed significant spatial autocorrelation of multi- year average forest carbon sequestration (Fig. 6, c). The hot and cold spots did not undergo any significant shift. Enshi and Shiyan were the hotspots of carbon sequestration with significant spatial autocorrelation.
According to the trend analysis of forest carbon sequestration from 2000 to 2020 (Fig. 6, b), it has been observed that the overall change of forest carbon sequestration in Hubei has demonstrated significant improvement over time. Western regions, including Shiyan, Enshi, Yichang and certain parts of Xiangyang, showed a significantly increasing trend of forest carbon sequestration. In contrast, Suizhou, Xianning, Xianning and Huanggang exhibited varying degrees of the increasing trends.
3.3. The offsetting effect of forest carbon sequestration on energy carbon emissions
According to the trend analysis of the offsetting effect of forest car- bon sequestration on energy carbon emissions (Fig. 7), the effect value of Hubei Province experienced a significant decline from 2000 to 2020.
Offsetting effect changing trend could be divided into two stages, with 2005 as the turning point. During the initial stage (2000–2005), the offsetting effect was greater than zero, indicating that forest carbon sequestration could offset energy carbon emissions and Hubei Province acted as a carbon sequestration area. However, during the second stage
(2005–2020), the offsetting effect reached the critical point and since then, the effect value has been less than zero, indicating that Hubei Province became a carbon emission source. Hubei’s carbon footprint was gradually surpassing its capacity and causing ecological stress, which is consistent with Li et al. (2022).
Comparing the spatial maps of the offsetting effects in some special years (2000, 2005, 2006, and 2020), it was apparent that the offsetting effect was more stronger in the western regions than in the eastern re- gions (Fig. 8). The offsetting effect was significantly strong in areas such as Shiyan, Shennongjia, Enshi, Xiangyang, and Yichang. However, it was becoming increasingly unfavorable in Wuhan as carbon emissions continue to rise due to the city’s development. Overall, the changes in Hubei have become more evident and demonstrate a weakening trend over time, consistent with the findings depicted in Fig. 7.
4. Discussion
4.1. Spatio-temporal patterns of forest carbon sequestration and energy carbon emissions and their causes
4.1.1. Spatio-temporal patterns of energy carbon emissions and cause Correlation analysis indicated that energy carbon emission is posi- tive related with GDP and population size (Table A3), suggesting that carbon emissions are driven by economic development and population growth. This result is consistent with the national level results such as Zheng et al. (2019) and Ma et al. (2019). Meanwhile, some studies (such as Li et al., 2022; Yang et al., 2022) reported the spatial match among carbon emissions and population, GDP, and industrial structure, which could supported our results. Carbon emissions varied significantly among different regions in the same year (Jiang et al., 2017). Comparing Fig. 4.Spatial map of multi-year average carbon emissions caused by industrial energy consumption (a), and spatial map of annual carbon emission changing trend in Hubei Province from 2000 to 2020 (b). Hotspot analysis of multi-year average carbon emissions (c).
J. Cheng et al.
the energy carbon emissions and GDP data in Hubei Province in one year, regions with high carbon emissions usually had higher GDP per capita, and the regional economic growth differences could affect the carbon emissions from industrial energy, which may be associated with variations in regional economic development (Kais and Sami, 2016).
Increasing energy inputs raised economic output while aggravated carbon emissions (Waheed et al., 2019). Although the carbon emissions of Hubei Province increased, the carbon emission intensity (ratio of carbon emission and GDP) decreased, revealing that the carbon emission intensity growth rate of Hubei Province tapered off nearly two decades.
As reported by the International Energy Agency (IEA), there was a very noticeable decline in the per capita carbon emissions of Hubei since 2010, promoting high-quality development (Liu et al., 2017).
Energy carbon emissions exhibited significant spatial heterogeneity, with central and eastern regions emitting more than the western regions in western Hubei (Fig. 4,a), which may had a few reasons. Firstly, combined with industry pattern of Hubei Province (Appendixes.docx), energy-intensive industries such as equipment and machinery, aero- space and other industries were concentrated in the central and eastern regions, while the western was dominated by light industries such as garments, food and pharmaceuticals, and relatively speaking, carbon emissions were lower in the west. Secondly, the western topographic characteristic is mainly mountains, making it less suitable for large-scale population gathering and less influenced by human activities. While the eastern region is mostly plains, combined with the Jianghan Plain is fertile and rich in resources, leading to an agglomeration effect (Chen et al., 2021). This not only increased energy demand and consumption scale but also aggravated carbon emissions due to population agglom- eration. Relevant studies reported that the synergistic relationship be- tween population size and environment usually contributes to more in the environmental degradation than population size (Liang and Yang,
2019). Additionally, transportation, construction, and household waste also improve environmental stress.
Meanwhile, energy carbon emissions also exhibited a significant spatial aggregation (Fig. 4,b). During 2000–2020, eastern regions had notably higher energy carbon emissions compared the western regions, which could be attributed to the similar levels of economic develop- ment, population size, and industrial activities in the surrounding cities and states. The population in the eastern was more concentrated, and Wuhan lead the economic development with the surrounding areas by taking advantage of the role of “one main leader”, playing the role of economic radiation (Wang et al., 2022a). Furthermore, economic development was more better in the eastern regions than the western regions, and the rapid economic growth of the eastern regions had aggravated energy consumption and carbon emissions with the rapid urbanization process and the urban agglomeration effect.
4.1.2. Spatio-temporal patterns of forest carbon sequestration and cause Correlation analysis documented that forest carbon sequestration was positively related to GDP with the Pearson correlation coefficient of 0.584 (Table A3), which is consistent with the findings of Yao et al.
(2018). Over 50% of population flowed into Wuhan, and forest area increased but population decreased in the western regions during 2011–2012 (Chen et al., 2014), thereby enhancing forest carbon sequestration. Guided by macro policies, Hubei Province has imple- mented ecological restoration, reforestation, and natural forest conser- vation, reaching 116 million acres of forest area, 415 million m3 of forest storage, and 41.84% of forest coverage in 2021.
The spatial map of multi-year average forest carbon sequestration exhibited a differentiated pattern, and it was higher in the western re- gions and in the eastern regions. The variation may be due to the combined influence of topography, economic development level, and Fig. 5.Temporal variations of forest carbon sequestration in Hubei from 2000 to 2020. Note: Blue point is annual forest carbon sequestration of the Hubei province.
Red dotted line indicates the change trend of forest carbon sequestration, and the gray region is the 95% confidence intervals of the linear model. The changing trend is described by the modelled slope (Mt/yr) of the least-square linear regression model, and the t statistic to test the significance of the changing trend, which is documented by the p-value. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Journal of Cleaner Production 426 (2023) 139147
policy orientation. Due to population growth, the flat topographic areas in the eastern regions have led to urban expansion, forming a re- attractive effect on population and industry, leading to phenomena such as forest to construction land conversion, and land utilization types were predominantly urban, with a generally lower forest carbon sequestration in the western regions (Yang et al., 2020). Western region was less affected by human activities owing to its topography, with a better vegetation cover and forest size compared to the eastern region.
As a key carbon sequestration influencing factors, the land use degree is usually inversely proportional to carbon sequestration, urban expansion decreased forest carbon sequestration (Zhang et al., 2020; Yang et al., 2022). The topographical characteristic influences the landscape pattern and land use degree, leading the various forest carbon sequestrations generated by differentiated regional development.
Forest carbon sequestration in Hubei Province presented significant spatial aggregation, which was closely related to the natural environ- ment in the western regions (Fig. 6, c). As of 2018, the Department of Ecology and Environment of Hubei compiled a list of 82 nature reserves (http://sthjt.hubei.gov.cn), and most of these reserves situated in the west and boasting high vegetation cover with higher NPP. Therefore, protecting these natural reserves and maintaining ecological balance is essential for sustainable forest carbon sequestration of the Hubei Province.
4.2. Mechanism of the role of forest carbon sequestration on energy carbon emission at regional scale
Earth is a closed system where the atmospheric carbon cycle is completed (Berner, 2003). Industrial carbon emissions and forest carbon sequestration both could impact atmospheric carbon pool. Carbon
emissions have increased atmospheric CO2 concentrations in recent decades, leading climate problems such as greenhouse effect and global warming (Abbasi et al., 2022). While regulating effects of forest carbon sequestration on atmospheric CO2 concentration is limited (Malhi et al., 2002). Rapid economic growth of Hubei Province in recent decades has increased significantly carbon emissions (Wang et al., 2022b), resulting in varying levels of energy carbon emissions across different regions and periods. However, Hubei government also continuously carried out vegetation restoration projects including afforestation, natural forest protection and ecological network construction (Wang et al., 2021).
These efforts played a key role in carbon compensation and have alle- viated some carbon emissions, enabling Hubei Province to meet green emission reduction targets.
During 2000–2005, forest carbon sequestration could offset indus- trial carbon emissions (Fig. 7). Despite facing challenges such as over- capacity, low industrial level, low market share and insufficient industrial innovation capacity, forests helped to control the industrial carbon emission growth rate (Koley, 2022; Liu et al., 2022). However, it was difficult to offset industrial carbon emissions during 2006–2020 (Fig. 7), and the gap between forest carbon sequestration and industrial carbon emission has become larger and larger due to Hubei Province’s vigorous economic development since 2005. The rise in planned in- dustries has increased carbon emissions, creating a widening gap be- tween carbon sequestration and carbon emission. As a result, it has become more challenging for the forest to keep up with the growing industrial emissions under the guidance of the industrial economy.
Furthermore, if we focused on the duration of 2000–2010, the off- setting effect could reach on a balance, meaning the positive effect value of 2000–2005 could fill the gap caused by the negative effect value of 2005–2010 (Fig. 7). However, the gap gradually increased from 2010 to Fig. 6. Spatial map of multi-year average forest carbon sequestration (a), and spatial map of annual forest carbon sequestration changing trend in Hubei Province from 2000 to 2020 (b). Hotspot analysis of multi-year average forest carbon sequestration (c).
J. Cheng et al.
Fig. 7. Temporal variations of the offsetting effect of forest carbon sequestration on industrial CO2 emissions of Hubei from 2000 to 2020. Note: Blue point is the offsetting effect, and the red dotted line indicates the change trend of the offsetting effect. Gray region is the 95% confidence intervals of the linear model of offsetting effect. The changing trend is described by the modelled slope (Mt/yr) of the least-square linear regression model, and the t statistic to test the significance of the changing trend, which is documented by the p-value. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 8. Spatial maps of the offsetting effect of forest carbon sequestration on carbon emissions of Hubei Province in 2000 (a), 2005 (b), 2006 (c) and 2020 (d).
Journal of Cleaner Production 426 (2023) 139147 2020, and energy carbon emissions have consistently been greater than
carbon sequestration, and the offsetting effect value is negatively compensated. Therefore, carbon neutrality should be a sustainable long- term objective (Zhao et al., 2022), allowing for small fluctuations in the offsetting effect in a given year while achieving carbon emissions equal to or even less than carbon neutrality for a significant time.
4.3. Carbon trading and carbon offsetting insights and measures Carbon trading is a new market-based mechanism that facilitates a win-win situation in terms of cost and resource utilization by trans- forming CO2 into a commodity and promoting CO2 emission rights to be traded, which facilitates the low-carbon transition of regions and countries (Perdan and Azapagic, 2011). Over two-thirds of countries have already adopted carbon trading market to fulfill their national obligations under the Paris Agreement by 2022 (Kong and Wang, 2022).
Carbon trading policies have played a significant role in reducing emissions in China (Zhang et al., 2020). Defined as “the act of compen- sating carbon sequestration or ecological protectors in an economic or non-economic manner by carbon emitters”, the forest offsetting effect is a method that takes advantage of the differences in carbon sources and sequestration across various regions to form carbon trading prices (Koley, 2022; Zhao et al., 2010). These methods meet the interests and participation of stakeholders, while also promoting low-carbon devel- opment at the regional scale.
On the one hand, setting up a digital and transparent carbon emis- sion tracking and accounting system and improving the regional emis- sion system standards. Jordan (World Bank Open Data | Data) has established an MRV system to track carbon emissions from diverse sectors in line with international standards, making it the first devel- oping country to establish a digital infrastructure to track and trade global GHG emission reductions. In 2020, Chinese government launched a decade-long national energy sector strategy to improve its energy mix with a plan to reduce carbon emissions over 10 years, and the MRV system has now been expanded to 22 agencies and ministries (Yang et al., 2020). The digital carbon accounting system can provide timely feedback to market participants, facilitating the government’s macro-regulation and top-level design role by collecting and aggre- gating carbon emission-related data and feeding back carbon trading dynamics (Yan et al., 2023).
On the other hand, it should strengthen the reform based on the regionalized carbon trading market mechanism and explore the devel- opment direction for the national carbon trading market (Wang et al., 2022d). The world’s largest carbon emissions trading market, to implement the commitments of the Kyoto Protocol by establishing the EU Emissions Trading Scheme, whose structure is relatively decentral- ized to allow member states to play a flexible and autonomous role (Ellerman, 2010). Differing from national carbon trading markets, local market should include variations in industry coverage, regional trading policies, and enterprise emission thresholds, etc (Zhou and Li, 2019).
Piloting the former is of great reference for the establishment of the latter, and it is recommended to carry out radical reforms in local carbon markets in terms of industry coverage, enterprise inclusion thresholds, allowance allocation methods and trading subjects, providing a basis for the development and improvement of the latter.
Besides, carbon emissions are mainly derived from energy con- sumption. Energy carbon emission reduction and low-carbon develop- ment measures are essential in high-emissions cities. Energy emission reduction and low-carbon development measures are as follows: (1) Utilization of energy development: improving energy conversion effi- ciency, optimizing energy utilization structure, and increasing the pro- portion of renewable energy (Acheampong, 2018). (2) Low-carbon production: improving the production standard system, benchmarking the industry’s high-standard production technology, upgrading product equipment, coordinating energy saving and carbon reduction and recycling (Wang et al., 2022b). (3) External support: reinforcing the
support and guarantee role of policies, funds and technologies for in- dustrial development (Liu, 2014).
Increasing forest carbon stocks is the most cost-effective way to offset carbon emissions, while forests lacking artificial management are often the most ineffective. Thus, actively carry out vegetation management in cities with a high proportion of forests can enhance forest carbon sequestration and forest biomass so as to maintain the carbon seques- tration rate of forest ecosystems (Van Kooten and Johnston, 2016).
4.4. Limitations and uncertains
Although we tried the best to assess forest carbon sequestration, some limitations still should be noted. On the one hand, the uncertainty of data acquisition. NPP vary depending on factors such as vegetation type, growth, and the environment used for measurement, leading to errors and uncertainty in its acquisition (Xu et al., 2020). Our NPP data were acquired from the MODIS production, and remote sensing moni- toring may be subject to differences and uncertainties (Sun et al., 2021).
On the other hand, we used forest NPP to calculate the forest carbon sequestration without considering the consumption of heterotrophic respiration. It is more accurate to use net ecosystem productivity which is the difference gross primary production and total ecosystem respira- tion for indicating the net carbon storage of forests at large scales (Qiu et al., 2023).
Meanwhile, this study just focused on industrial carbon emissions, while other sources, including buildings, transportation, agriculture, and land use, also play a role in carbon emissions, different sources of data on carbon emissions could be studied in the future (Waheed et al., 2019). Further research could expand the scope of carbon emissions, have a more accurate comparison of changes in carbon emissions from different sources, and have a more nuanced differentiation of carbon footprints. Besides, we propose to analyze the carbon emission gap and thus provide targeted guidance for carbon emission reduction.
5. Conclusions
This paper examined the relationship between forest carbon sequestration and industrial carbon emissions in Hubei Province from 2000 to 2020. The results indicated that energy carbon emissions and forest carbon sequestration of Hubei Province both increased from 2000 to 2020, with the growth rates of 4.9423 Mt/yr and 0.28015 Mt/yr.
Forest carbon sequestration could effectively offset industrial carbon emissions before 2005. However, this effect weakened after 2005 due to the continuous increase in industrial production and economic devel- opment. Hubei Province gradually transformed from “carbon seques- tration” to “carbon source” region, which was closely related to the steady increase in industrial carbon emissions. The results speculated that even with carbon peaks by 2030, CO2 emissions from energy con- sumption through industrial activities will continuously increase, and the offsetting effect of forest carbon sequestration will continue to decline. In light of the situation facing development and the high-quality development based on the carbon peaking and carbon neutrality goals, it is imperative that reasonable measures and actions be taken to address this situation. As forest carbon sequestration is the most cost-effective way to offset industrial carbon emissions, our study recommends con- structing forest cities vigorously and enhancing urban forests’ carbon sequestration. This study conducted the carbon footprints of various activities in Hubei Province and whether forest offsetting effects were sufficient to achieve net-zero emissions, offering innovative case studies, as well as exploring new ideas for regional sustainable development based on our findings.
Ethical approval Not applicable.
J. Cheng et al.
Consent to participate Not applicable.
Consent to publish Not applicable.
Funding
This work is sponsored by the National Natural Science Foundation of China (Grant number 42001218) and the General Project of Hubei Social Science Fund (Grant number 2021211, HBSK2022YB357).
CRediT authorship contribution statement
Jing Cheng: Writing – original draft, preparation. Chunbo Huang:
Conceptualization, Writing – review & editing, Supervision. Xintao Gan: Data collection, Formal analysis, Writing – original draft, prepa- ration. Changhui Peng: Validation, Conceptualization, and design. Lei Deng: Conceptualization, and design.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
No data was used for the research described in the article.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jclepro.2023.139147.
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