Research on Ecological Compensation for Construction Land from a Carbon Emission Perspective

Research on Ecological Compensation for Construction Land from a Carbon Emission Perspective | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Research on Ecological Compensation for Construction Land from a Carbon Emission Perspective Na Lei, Yang Zhang, Juan Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2161826/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Construction lands are the main sources of carbon emissions. In this study, data on the energy consumption, permanent population, and gross domestic product (GDP) of Shaanxi Province from 2010 to 2018 were collected. Using a carbon emission assessment model, the emissions from all the cities and the demonstration area in Shaanxi Province were evaluated. Ecological compensation standards for carbon emissions were determined. The analyses showed the following results: (1) From 2010 to 2018, the total and per capita carbon emissions from construction land showed an upward trend. Generally, the carbon emissions per unit GDP for all the cities and the demonstration area in Shaanxi Province showed a downward trend. (2) The total, per capita, and per unit GDP regional carbon emissions in Shaanxi Province varied significantly. In 2010 and 2018, Yulin and Yangling Demonstration Area showed the highest and lowest total carbon emissions, respectively. Yulin and Shangluo showed the highest and lowest per capita carbon emissions, respectively. In 2010, the highest and lowest carbon emissions per unit GDP were from Weinan and Xi’an, respectively, whereas in 2018, they were from Tongchuan and Xi’an, respectively. (3) The construction land area correlates with the carbon emissions from Shaanxi Province between 2010 and 2018, and the correlation coefficient is 0.9248. The fitted function can be used as a model for predicting carbon emissions and can provide a scientific basis for quantitative accounting of carbon emissions from construction land. (4) According to moderate estimates, the ecological compensation that should be provided by all the cities and the demonstration area in Shaanxi Province for 2018 is 1.29 to 1.44 times that for 2010. The growth periods were the shortest and longest for Yangling Demonstration Area and Tongchuan, respectively. These results can act as a reference to plan low-carbon, green, and sustainable economic development in Shaanxi Province. Biological sciences/Ecology Biological sciences/Evolution Earth and environmental sciences/Ecology Carbon emissions construction land ecological compensation Shaanxi Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Global warming has become a serious ecological problem. Human activities have led to the emission of large amounts of carbon dioxide and other greenhouse gases into the atmosphere 1 – 3 . The trend of global warming is indisputable 4 , 5 . Carbon emissions are an important indicator of greenhouse gas emissions 6 . Construction lands are the main sources of carbon emissions 7 , because they use a large amount of energy 8 , 9 . Since the beginning of the 20th century, owing to the rapid development of economies, urbanization, which is accompanied by continuous expansion of construction land, has become a development trend 10 , 11 . Such rapid expansion of construction land has caused a sharp increase in material and energy consumption and further led to a continuous increase in carbon emissions 12 . Studies have shown that 80–90% of carbon emissions come from consumption of fossil fuels for energy 13 . In the context of green and low-carbon global development, the Chinese government has formulated a plan to deal with climate change to fulfill international responsibilities. It has promised to reduce carbon emissions per unit gross domestic product (GDP) and, at the same time, proposed the incorporation of carbon emissions as a binding indicator into the long-term plan for national economic and social development and the implementation of green GDP accounting every year 14 . Therefore, mastering the law of carbon emissions from construction land and formulating ecological compensation measures are essential for the rational use of energy during construction land utilization and for further improving the efficiency of energy use. At present, research on carbon emissions mainly focuses on quantitative and spatiotemporal analysis of land using information technology such as RS (remote sensing) and GIS (geographical information system) 15 – 19 . Research on carbon-emitting regions has mainly concentrated on provinces and hot cities 20 – 24 . Some scholars have studied the positive correlation between carbon emissions and economic growth 25 , whereas others have used various models to simulate and predict future carbon emissions 26 . Thus, current research mainly focuses on the impact of land use on carbon emissions. There have been few studies on the relationship between carbon emissions from construction land and ecological compensation and on the spatial changes in carbon emissions. Therefore, in this study, all the cities and the demonstration area of Shaanxi Province in China were taken as research objects, the relationship between construction land and carbon emissions from 2010 to 2018 were analyzed, and the corresponding ecological compensation was determined. The temporal and spatial changes in carbon emissions from construction land were accurately mapped. The results will aid decision-making for formulating reasonable ecological compensation measures and systems. Materials And Methods Overview of study area Shaanxi Province is located in the middle reaches of the Yellow River, in the hinterland of China (105°29′-111°15′E, 31°42′-39°35′E). It is adjacent to Shanxi and Henan in the east, Ningxia and Gansu in the west, Sichuan, Chongqing, and Hubei in the south, and Inner Mongolia in the north, with a total area of 205,600 km 2 . The topography of Shaanxi Province includes plateaus, mountains, plains, and basins. Generally, its altitude is high in the north and south and low in the middle, with a range of 460–3000 m. The Loess Plateau occupies 40% of the province’s land area. It straddles the Yellow River and Yangtze River and has three climatic zones, namely, the middle temperate zone (northern Shaanxi), warm temperate zone (Guanzhong and most of northern Shaanxi), and subtropical monsoon zone (southern Shaanxi). The climate is dry with little rain in spring, hot and rainy in summer, cool and humid in autumn, and cold and dry in winter, with an average annual temperature of 9–16°C. Precipitation is more in the south and less in the north. Southern Shaanxi, Guanzhong, and northern Shaanxi are humid, semi-humid, and semi-arid areas, respectively, with an average annual rainfall of 340–1240 mm. Shaanxi Province has 10 prefecture-level cities, namely, Xi’an, Tongchuan, Baoji, Xianyang, Weinan, Yan’an, Hanzhong, Yulin, Ankang, and Shangluo, one provincial-level demonstration area (Yangling), 30 Municipal districts, 6 county-level cities, and 71 counties. In 2010 and 2018, the permanent population was 37,352,300 and 38,643,900, respectively, and the corresponding GDP was 1,012,348 million Yuan and 2,443,832 million Yuan. Data sources Data were obtained from the “ Statistical Yearbook of Shaanxi Province ” (2011–2019) and literature. Research methods Carbon emissions from construction land in all the cities and the demonstration area of Shaanxi Province were calculated using the shadow price method. Specifically, the carbon emissions due to energy consumption during construction land utilization were calculated. To facilitate quantitative comparison, the total energy consumption was uniformly expressed in terms of standard coal. The formula used for the calculation is expressed as follows: $$E=M \times F$$ 1 Where E is the carbon emission from construction land (× 10 4 t), F is the carbon emission conversion coefficient of coal consumption (0.733), and M is the energy consumption in terms of standard coal (× 10 4 t). Carbon sink prices were used to determine the eco-compensation for carbon emissions. The most widely used afforestation cost method and carbon tax rate method were used to calculate the carbon sequestration value. The internationally accepted Swedish carbon tax rate, USD 150/t C (RMB 1,026 Yuan/t C), and the afforestation cost of planting forests in China, RMB 122 Yuan/t C, were used to determine the upper and lower limits, respectively, of ecological compensation; China’s average afforestation cost of RMB 272.65 Yuan/t C was adopted to determine the reasonable price of ecological compensation. Results And Analysis Construction land carbon emissions and time differences The carbon emissions of construction lands in all the cities and the demonstration area in Shaanxi Province show an upward trend from 2010 to 2018. They were 73.307 and 98.8604 million tons in 2010 and 2018, respectively. Compared with those in 2010, the carbon emissions from construction land in 2018 had increased by 34.86%. Compared with those in 2010, the carbon emissions from construction land in Xi’an, Tongchuan, Baoji, Xianyang, Weinan, Yan’an, Hanzhong, Yulin, Ankang, Shangluo, and Yangling Demonstration Area in 2018 had increased by 37.11%, 44.99%, 34.33%, 29.74%, 33.16%, 30.82%, 40.49%, 33.22%, 39.47%, 35.82%, and 29.72%, respectively (Table 1 ). Table 1 Carbon emissions from construction lands in all cities and the demonstration area in Shaanxi Province from 2010 to 2018 (× 10 4 t) Administrative District 2010 2011 2012 2013 2014 2015 2016 2017 2018 Xi’an 1594.27 1651.03 1708.98 1769.99 1874.24 1934.21 2008.30 2062.38 2185.92 Tongchuan 248.88 257.88 267.22 281.41 300.04 319.84 332.38 340.41 360.84 Baoji 591.53 612.41 634.71 657.11 684.05 708.61 739.86 759.47 794.63 Xianyang 600.51 622.19 643.97 666.51 688.84 712.19 736.40 756.13 779.11 Weinan 1139.59 1180.62 1223.24 1269.84 1324.83 1367.35 1418.36 1448.79 1517.46 Yan’an 419.24 433.91 449.10 464.82 480.62 498.89 516.35 532.89 548.45 Hanzhong 503.54 521.72 540.29 559.25 581.90 610.77 639.23 673.21 707.41 Yulin 1810.67 1875.85 1943.39 2017.23 2093.89 2167.18 2238.69 2337.00 2412.25 Ankang 221.41 229.16 237.47 245.38 256.42 266.68 278.71 298.51 308.81 Shangluo 169.40 175.33 181.26 187.27 193.04 198.77 206.01 216.89 230.08 Yangling 31.66 32.67 33.53 34.40 35.66 36.41 37.16 39.87 41.07 Total 7330.70 7592.77 7863.14 8153.22 8513.54 8820.90 9151.43 9465.56 9886.04 Figure 1 presents the carbon emissions per capita and per unit GDP for Shaanxi Province from 2010 to 2018. The per capita carbon emissions show a continuous increase from 21.76 to 29.04 t/person. Compared with those in 2010, the per capita carbon emissions of construction land in 2018 had increased by 33.46%. Carbon emissions per unit GDP showed an overall downward trend from 0.072 to 0.040 kg/yuan. The carbon emissions per unit GDP for construction land had dropped by 44.44% in 2018 compared with that in 2010; before 2014, the decline was large, whereas after 2014, the decline began to slow down. Spatial differences in carbon emissions from construction lands in all cities and the demonstration area In 2010 and 2018, the carbon emissions from construction lands in Shaanxi Province were the largest in Yulin, at 1810.67 and 2412.25 t, respectively. In 2010, the order of carbon emissions from construction land was as follows: Yulin > Xi’an > Weinan > Xianyang > Baoji > Hanzhong > Yan’an > Tongchuan > Ankang > Shangluo > Yangling Demonstration Area. In 2018, the order was Yulin > Xi’an > Weinan > Baoji > Xianyang > Hanzhong > Yan’an > Tongchuan > Ankang > Shangluo > Yangling Demonstration Area. Carbon emissions continued to increase owing to sustained development as a result of urbanization and industrialization. Among the cities and the demonstration area in Shaanxi Province, Yulin produced the highest carbon emissions from construction land, which increased from 1810.67 t in 2010 to 2412.25 t in 2018, with an average annual increase of 4.16 t. In contrast, the lowest emissions were from Yangling Demonstration Area; they increased from 31.66 t in 2010 to 41.07 t in 2018, with an average annual increase of 4.95 t. In 2018, Yulin and Xi’an produced carbon emissions greater than 2000 t; Weinan, Baoji, Xianyang, Hanzhong, and Yan’an produced emissions greater than 500 t; and Tongchuan, Ankang, Shangluo, and Yangling Demonstration Area produced emissions less than 500 t. The Yangling Demonstration Area produced less than 50 t of carbon emissions. In 2010, Yulin, Xi’an, and Weinan produced carbon emissions greater than 1,000 t; Baoji, Xianyang, and Hanzhong produced emissions greater than 500 t; and Tongchuan, Ankang, Shangluo, and Yangling Demonstration Areas produced emissions less than 500 t. The Yangling Demonstration Area again produced less than 50 t of carbon emissions (Table 2 ). Table 2 Carbon emissions from construction lands in Shaanxi Province in 2010 and 2018 Administrative District Carbon emissions (t) Per capita carbon emissions (t/person) Per unit GDP carbon emissions (kg/Yuan) 2010 2018 2010 2018 2010 2018 Xi’an 1594.27 2185.92 1.88 2.19 0.049 0.026 Tongchuan 248.88 360.84 2.98 4.49 0.133 0.110 Baoji 591.53 794.63 1.59 2.11 0.061 0.035 Xianyang 600.51 779.11 1.23 1.78 0.055 0.033 Weinan 1139.59 1517.46 2.15 2.85 0.142 0.086 Yan’an 419.24 548.45 1.92 2.43 0.047 0.035 Hanzhong 503.54 707.41 1.47 2.06 0.099 0.048 Yulin 1810.67 2412.25 5.40 7.06 0.103 0.063 Ankang 221.41 308.81 0.84 1.16 0.068 0.027 Shangluo 169.40 230.08 0.72 0.97 0.059 0.028 Yangling 31.66 41.07 1.57 1.96 0.067 0.027 There were obvious regional differences in carbon emissions per capita and per unit GDP in Shaanxi Province. In 2010 and 2018, the per capita carbon emissions from Yulin, at 5.40 and 7.06 t, respectively, were significantly higher than those from other cities. In 2010, the order of per capita carbon emissions was Yulin > Tongchuan > Weinan > Yan’an > Xi’an > Baoji > Yangling Demonstration Area > Hanzhong > Xianyang > Ankang > Shangluo. In 2018, the order was basically the same as that in 2010. The per capita carbon emissions in 2010 were greater than 5 t for Yulin; greater than 2 t for Tongchuan and Weinan; greater than 1 t for Yan’an, Xi’an, Baoji, Yangling Demonstration Area, Hanzhong, and Xianyang; and less than 2 t for Ankang and Shangluo. In 2018, the per capita carbon emissions were greater than 7 t for Yulin; greater than 2 t for Tongchuan, Weinan, Yan’an, Xi’an, Baoji, and Hanzhong; greater than 1 t for Yangling Demonstration Area, Xianyang, and Ankang; and less than 1 t for Shangluo. In 2010, the largest and smallest carbon emissions per unit GDP in Shaanxi Province were exhibited by Weinan (0.142 kg/yuan) and Xi’an (0.049 kg/yuan), respectively. The order of carbon emissions per unit GDP was as follows: Weinan > Tongchuan > Yulin > Hanzhong > Ankang > Yangling > Baoji > Shangluo > Xianyang > Xi’an > Yan’an. In 2018, the order of per capita carbon emissions was Tongchuan > Weinan > Yulin > Hanzhong > Baoji = Yan’an > Shangluo > Ankang = Yangling Demonstration Area > Xi’an (Table 2 ). Construction land carbon emission intensity and prediction model Construction land area and carbon emissions increased in Shaanxi Province from 2010 to 2018. Compared with that in 2010, the construction land area in 2018 had increased by 18.46% (Fig. 2). We found that there was an exponential correlation between the construction land area and carbon emissions; the correlation coefficient was 0.9248 (Fig. 3). The fitted function can be used as a model for predicting carbon emissions in the future and can provide a scientific basis for quantitative accounting of carbon emissions from construction land. The carbon emission intensity of construction land can be expressed as carbon emissions per unit construction land area. From 2010 to 2018, the carbon emission intensity of construction lands in Shaanxi Province showed an increasing trend. Compared with that in 2010, the carbon emission intensity of construction land in 2018 had increased by 13.85% (Fig. 4). The regional differences in carbon emission intensity of construction lands in Shaanxi Province were obvious. In 2010 and 2018, Yulin exhibited the highest carbon emission intensity, at 21.99 and 19.55 t/hm 2 , respectively; Yangling Demonstration Area exhibited the lowest carbon emission intensity, at 0.34 and 0.37 t/hm 2 , respectively. In 2010, the carbon emission intensity was greater than 15 t/hm 2 in Yulin and Xi’an, greater than 10 t/hm 2 in Weinan, greater than 5 t/hm 2 in Xianyang, Baoji, and Hanzhong, and lesser than 5 t/hm 2 in Yan’an, Tongchuan, Ankang, Shangluo, and Yangling Demonstration Area. In 2018, the carbon emission intensity was greater than 15 t/hm 2 in Yulin and Xi’an, greater than 10 t/hm 2 in Weinan, greater than 5 t/hm 2 in Baoji, Xianyang, Hanzhong, and Yan’an, and lesser than 5 t/hm 2 in Tongchuan, Ankang, Shangluo, and Yangling Demonstration Area (Fig. 5). Ecological compensation standard for carbon emissions The ecological compensation quotas of all the cities and the demonstration area in Shaanxi Province for 2010 and 2018 were obtained using the carbon emissions from construction lands and three types of carbon sequestration prices (Table 3 ). Table 3 Ecological compensation quotas for construction lands in Shaanxi Province for 2010 and 2018 Administrative District Total carbon emissions (× 10 4 t) Lower limit (× 10 8 Yuan) Moderate amount (× 10 8 Yuan) Upper limit (× 10 8 Yuan) 2010 2018 2010 2018 2010 2018 2010 2018 Xi’an 1594.27 2185.92 19.45 26.67 43.47 59.60 163.57 224.28 Tongchuan 248.88 360.84 3.04 4.40 6.79 9.84 25.53 37.02 Baoji 591.53 794.63 7.22 9.69 16.13 21.67 60.69 81.53 Xianyang 600.51 779.11 7.33 9.51 16.37 21.24 61.61 79.94 Weinan 1139.59 1517.46 13.90 18.51 31.07 41.37 116.92 155.69 Yan’an 419.24 548.45 5.11 6.69 11.43 14.95 43.01 56.27 Hanzhong 503.54 707.41 6.14 8.63 13.73 19.29 51.66 72.58 Yulin 1810.67 2412.25 22.09 29.43 49.37 65.77 185.77 247.50 Ankang 221.41 308.81 2.70 3.77 6.04 8.42 22.72 31.68 Shangluo 169.40 230.08 2.07 2.81 4.62 6.27 17.38 23.61 Yangling 31.66 41.07 0.39 0.50 0.86 1.12 3.25 4.21 Yulin should provide the highest ecological compensation for 2018, with an amount ranging from 29.43 × 10 8 to 247.50 × 10 8 Yuan, followed by Xi’an, Weinan, and Baoji. Based on the average cost of afforestation in China, the latter three cities should provide ecological compensations of 59.6 × 10 8 , 41.37 × 10 8 , and 21.67 × 10 8 Yuan, respectively. Yangling Demonstration Area should provide the lowest ecological compensation, with an amount ranging from 0.5 × 10 8 to 4.21 × 10 8 Yuan. The order of ecological compensation standards for all the cities and the demonstration area in Shaanxi Province was basically the same for 2010 and 2018. The moderate amounts of ecological compensation that should be provided by all the cities and the demonstration area in Shaanxi Province for 2018 is 1.29 to 1.44 times that for 2010. Among the administrative districts, Yangling Demonstration Area and Tongchuan exhibit the smallest and largest growth multiples, respectively. The growth periods were the shortest and longest for Yangling Demonstration Area and Tongchuan, respectively. Discussion From 2010 to 2018, carbon emissions from construction lands in Shaanxi Province increased but carbon emissions per unit GDP decreased overall. Owing to continuous economic development, the demand for construction land increased, and in turn increased carbon emissions. However, due to the development of national high technology, the energy consumption per unit GDP decreased. These findings were confirmed in this study. Yulin’s industry is relatively developed, and hence carbon emissions are large. Energy consumption per unit GDP has been the lowest in Xi’an. This is closely related to Xi’an’s high-tech development, emphasis on the use of eco-friendly energy, and positioning as an eco-tourism city. China should develop new products, technologies, and equipment, reduce carbon emissions per unit GDP, and implement national ecological protection policies. At this stage, the ecological compensation market in China is not perfect, and the compensation mechanism is not sound. Few regions implement ecological compensation 27 . It is basically still in the initial development stage. Compensation for returning farmland to forests, which is the rudimentary form of ecological compensation, is relatively mature. It is recommended to use the afforestation cost method to determine such compensation. Internationally, Germany started ecological compensation first, in the 1960s. The scope of ecological compensation has gradually expanded to land occupied by construction projects. Any loss of water resources, air quality, flora and fauna, and landscapes and other ecological environments needs to be compensated. The types of compensation have been gradually diversified and include in-situ compensation, relocation, payment, and combinations of these. The most important objective is to give full play to the market mechanism, establish a mature trading market for the ecological compensation index, and use the market to adjust the price of ecological compensation 28 . China can learn from the advanced experience of Germany and make efforts to improve ecological compensation from the perspectives of system, policy formulation, loss assessment, research on compensation methods, and improvement of compensation markets. Conclusion (1) From 2010 to 2018, the total and per capita carbon emissions from construction lands in the cities and the demonstration area of Shaanxi Province showed an upward trend. Compared with those in 2010, the total and per capita carbon emissions from construction land in Shaanxi Province in 2018 had increased by 34.86% and 33.46%, respectively. Carbon emissions per unit GDP showed a downward trend overall. Compared with those in 2010, carbon emissions per unit GDP in 2018 had dropped by 44.44%. Before 2014, the decline was large, whereas after 2014, the decline began to slow down. (2) There were obvious regional differences in the total, per capita, and per unit GDP carbon emissions from construction land in Shaanxi Province. In 2010 and 2018, the carbon emissions from construction land in Shaanxi Province were the largest in Yulin and the smallest in Yangling Demonstration Area; the per capita carbon emissions in Yulin were significantly higher than those in other urban areas. In 2010, the carbon emissions per unit GDP were the largest and smallest in Weinan and Xi’an, respectively. (3) The construction land area in Shaanxi Province correlated with the carbon emissions from 2010 to 2018, and the correlation coefficient was 0.9248. The fitted function can be used as a model for predicting carbon emissions and can provide a scientific basis for quantitative accounting of carbon emissions from construction land. Compared with that in 2010, the carbon emission intensity of construction land in 2018 had increased by 13.85%; regional differences in carbon emission intensity were obvious in Shaanxi Province. In 2010 and 2018, the carbon emission intensity of Yulin was the highest whereas that of Yangling Demonstration Area was the lowest. (4) For 2018, Yulin should provide the highest ecological compensation, whereas Yangling Demonstration Zone should provide the lowest. The order of ecological compensation standards for all the cities and the demonstration area in Shaanxi Province are basically the same for 2010 and 2018. The moderate amount of ecological compensation that should be provided by all the cities and the demonstration area in Shaanxi Province for 2018 is 1.29 to 1.44 times that for 2010. The growth period was shortest for Yangling Demonstration Zone and longest for Tongchuan. Declarations Acknowledgements This research was funded by This work was Supported by the Fundamental Research Funds for the Central Universities, CHD (300102352503); Shaanxi Provincial Land Engineering Construction Group fund (DJNY 2021-29); Natural Science Basic Research Program of Shaanxi (2021JQ-961). Author contributions statement L.N. wrote the main manuscript, L.N. and Z.Y. conceived the study, experimentation, Z.Y. revised manuscript and figures and L.J. analyzed the data. Additional Information Competing interests The authors declare no competing interests. Additional information The datasets used during the current study available from the corresponding author on reasonable request. References J. B. Li, X. J. Huang, M. P., Kwan, H. Yang, and X. M, Chuai, The effect of urbanization on carbon dioxide emissions efficiency in the Yangtze River Delta, China. Journal of Cleaner Production. 188, 38-48 (2018). Z. X., He, S. C., Xu, W. X., Shen, R. Y., Long, and H. 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Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACfmbmgw8kKiTs5NkbiNQi2c6WbGBxxiLZsOcAkVoM+nnMBCrbKhgbbiQQq4WZx4zhxhkJZsaZjzfeYKixiSaoxZyZrezhjAoJPnbptGILhmNpuQ2EtFg2M283lgDZMjvHTIKx4TBhLQaHGcyk/7YBFd88Q7QWFjMJSZCWGzxEapFsBgYy0GHAQAb6JYEYv/DzHwZFZR0wKg9vvPGhxoawFhRHSiSQohyihVQdo2AUjIJRMDIAAMuIPHLwZGkXAAAAAElFTkSuQmCC","orcid":"","institution":"Shaanxi Key Laboratory of Land Consolidation","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zhang","suffix":""},{"id":144696813,"identity":"920771de-ef2e-41aa-8c09-3e72f51ada79","order_by":2,"name":"Juan Li","email":"","orcid":"","institution":"Shaanxi Provincial Land Engineering Construction Group Co., Ltd","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2022-10-13 09:44:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2161826/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2161826/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27992301,"identity":"bc04d85f-c139-4ae9-a7c2-87a279b67888","added_by":"auto","created_at":"2022-10-19 14:14:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82875,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon emissions of per capita and per unit GDP from 2010 to 2018 in Shaanxi Province\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-2161826/v1/1903b578393336f2c8891793.png"},{"id":27993062,"identity":"cffcf80d-6139-421c-be75-6d05cb674294","added_by":"auto","created_at":"2022-10-19 14:19:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75243,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon emissions and area of construction land in Shaanxi Province from 2010 to 2018\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-2161826/v1/a4adea1f48b91e8362e29465.png"},{"id":27992305,"identity":"780c7b10-4764-4086-9c35-68ad83807134","added_by":"auto","created_at":"2022-10-19 14:14:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59305,"visible":true,"origin":"","legend":"\u003cp\u003eThe fitted function between Carbon emissions and area of construction land in Shaanxi Province\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-2161826/v1/7ed67436245f0c78f7999615.png"},{"id":27993063,"identity":"3f593c2b-c77a-405c-801c-ae794a110c6e","added_by":"auto","created_at":"2022-10-19 14:19:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43596,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon emissions intensity of construction land in Shaanxi Province from 2010 to 2018\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-2161826/v1/28c6649780eaa7fd685c4a8d.png"},{"id":27992304,"identity":"5b0cc70d-869f-4316-9169-23077679e020","added_by":"auto","created_at":"2022-10-19 14:14:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":632240,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon emissions intensity of construction land for all cities and demonstration area in Shaanxi in 2010and 2018\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-2161826/v1/a1604b3bffb27ca18157bc98.png"},{"id":28547722,"identity":"7038e026-51d0-45ad-b074-cc3e399b4a7c","added_by":"auto","created_at":"2022-11-02 07:29:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1215694,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2161826/v1/89090754-95df-4ddb-9564-e54a6e4938b8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Ecological Compensation for Construction Land from a Carbon Emission Perspective","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal warming has become a serious ecological problem. Human activities have led to the emission of large amounts of carbon dioxide and other greenhouse gases into the atmosphere\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The trend of global warming is indisputable\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Carbon emissions are an important indicator of greenhouse gas emissions\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Construction lands are the main sources of carbon emissions\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, because they use a large amount of energy\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Since the beginning of the 20th century, owing to the rapid development of economies, urbanization, which is accompanied by continuous expansion of construction land, has become a development trend\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Such rapid expansion of construction land has caused a sharp increase in material and energy consumption and further led to a continuous increase in carbon emissions\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Studies have shown that 80\u0026ndash;90% of carbon emissions come from consumption of fossil fuels for energy\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In the context of green and low-carbon global development, the Chinese government has formulated a plan to deal with climate change to fulfill international responsibilities. It has promised to reduce carbon emissions per unit gross domestic product (GDP) and, at the same time, proposed the incorporation of carbon emissions as a binding indicator into the long-term plan for national economic and social development and the implementation of green GDP accounting every year\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Therefore, mastering the law of carbon emissions from construction land and formulating ecological compensation measures are essential for the rational use of energy during construction land utilization and for further improving the efficiency of energy use.\u003c/p\u003e \u003cp\u003eAt present, research on carbon emissions mainly focuses on quantitative and spatiotemporal analysis of land using information technology such as RS (remote sensing) and GIS (geographical information system)\u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Research on carbon-emitting regions has mainly concentrated on provinces and hot cities\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Some scholars have studied the positive correlation between carbon emissions and economic growth\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, whereas others have used various models to simulate and predict future carbon emissions\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Thus, current research mainly focuses on the impact of land use on carbon emissions. There have been few studies on the relationship between carbon emissions from construction land and ecological compensation and on the spatial changes in carbon emissions. Therefore, in this study, all the cities and the demonstration area of Shaanxi Province in China were taken as research objects, the relationship between construction land and carbon emissions from 2010 to 2018 were analyzed, and the corresponding ecological compensation was determined. The temporal and spatial changes in carbon emissions from construction land were accurately mapped. The results will aid decision-making for formulating reasonable ecological compensation measures and systems.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOverview of study area\u003c/h2\u003e \u003cp\u003eShaanxi Province is located in the middle reaches of the Yellow River, in the hinterland of China (105\u0026deg;29\u0026prime;-111\u0026deg;15\u0026prime;E, 31\u0026deg;42\u0026prime;-39\u0026deg;35\u0026prime;E). It is adjacent to Shanxi and Henan in the east, Ningxia and Gansu in the west, Sichuan, Chongqing, and Hubei in the south, and Inner Mongolia in the north, with a total area of 205,600 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The topography of Shaanxi Province includes plateaus, mountains, plains, and basins. Generally, its altitude is high in the north and south and low in the middle, with a range of 460\u0026ndash;3000 m. The Loess Plateau occupies 40% of the province\u0026rsquo;s land area. It straddles the Yellow River and Yangtze River and has three climatic zones, namely, the middle temperate zone (northern Shaanxi), warm temperate zone (Guanzhong and most of northern Shaanxi), and subtropical monsoon zone (southern Shaanxi). The climate is dry with little rain in spring, hot and rainy in summer, cool and humid in autumn, and cold and dry in winter, with an average annual temperature of 9\u0026ndash;16\u0026deg;C. Precipitation is more in the south and less in the north. Southern Shaanxi, Guanzhong, and northern Shaanxi are humid, semi-humid, and semi-arid areas, respectively, with an average annual rainfall of 340\u0026ndash;1240 mm. Shaanxi Province has 10 prefecture-level cities, namely, Xi\u0026rsquo;an, Tongchuan, Baoji, Xianyang, Weinan, Yan\u0026rsquo;an, Hanzhong, Yulin, Ankang, and Shangluo, one provincial-level demonstration area (Yangling), 30 Municipal districts, 6 county-level cities, and 71 counties. In 2010 and 2018, the permanent population was 37,352,300 and 38,643,900, respectively, and the corresponding GDP was 1,012,348\u0026nbsp;million Yuan and 2,443,832\u0026nbsp;million Yuan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eData were obtained from the \u0026ldquo;\u003cem\u003eStatistical Yearbook of Shaanxi Province\u003c/em\u003e\u0026rdquo; (2011\u0026ndash;2019) and literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eResearch methods\u003c/h2\u003e \u003cp\u003eCarbon emissions from construction land in all the cities and the demonstration area of Shaanxi Province were calculated using the shadow price method. Specifically, the carbon emissions due to energy consumption during construction land utilization were calculated. To facilitate quantitative comparison, the total energy consumption was uniformly expressed in terms of standard coal. The formula used for the calculation is expressed as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$E=M \\times F$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere E is the carbon emission from construction land (\u0026times; 10\u003csup\u003e4\u003c/sup\u003e t), F is the carbon emission conversion coefficient of coal consumption (0.733), and M is the energy consumption in terms of standard coal (\u0026times; 10\u003csup\u003e4\u003c/sup\u003e t).\u003c/p\u003e \u003cp\u003eCarbon sink prices were used to determine the eco-compensation for carbon emissions. The most widely used afforestation cost method and carbon tax rate method were used to calculate the carbon sequestration value. The internationally accepted Swedish carbon tax rate, USD 150/t C (RMB 1,026 Yuan/t C), and the afforestation cost of planting forests in China, RMB 122 Yuan/t C, were used to determine the upper and lower limits, respectively, of ecological compensation; China\u0026rsquo;s average afforestation cost of RMB 272.65 Yuan/t C was adopted to determine the reasonable price of ecological compensation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results And Analysis","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eConstruction land carbon emissions and time differences\u003c/h2\u003e \u003cp\u003eThe carbon emissions of construction lands in all the cities and the demonstration area in Shaanxi Province show an upward trend from 2010 to 2018. They were 73.307 and 98.8604\u0026nbsp;million tons in 2010 and 2018, respectively. Compared with those in 2010, the carbon emissions from construction land in 2018 had increased by 34.86%. Compared with those in 2010, the carbon emissions from construction land in Xi\u0026rsquo;an, Tongchuan, Baoji, Xianyang, Weinan, Yan\u0026rsquo;an, Hanzhong, Yulin, Ankang, Shangluo, and Yangling Demonstration Area in 2018 had increased by 37.11%, 44.99%, 34.33%, 29.74%, 33.16%, 30.82%, 40.49%, 33.22%, 39.47%, 35.82%, and 29.72%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCarbon emissions from construction lands in all cities and the demonstration area in Shaanxi Province from 2010 to 2018 (\u0026times; 10\u003csup\u003e4\u003c/sup\u003e t)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" 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colname=\"c8\"\u003e \u003cp\u003e332.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e340.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e360.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaoji\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e591.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e612.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e634.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e657.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e684.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e708.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e739.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e759.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e794.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXianyang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e600.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e622.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e643.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e666.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e688.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e712.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e736.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e756.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e779.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeinan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1139.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1180.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1223.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1269.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1324.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1367.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1418.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1448.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1517.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYan\u0026rsquo;an\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e419.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e433.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e449.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e464.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e480.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e498.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e516.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e532.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e548.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHanzhong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e503.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e521.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e540.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e559.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e581.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e610.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e639.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e673.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e707.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1810.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1875.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1943.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2017.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2093.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2167.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2238.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2337.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2412.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnkang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e221.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e237.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e245.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e256.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e266.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e278.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e298.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e308.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShangluo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e181.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e187.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e193.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e198.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e206.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e216.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e230.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYangling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e41.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7330.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7592.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7863.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8153.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8513.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8820.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9151.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9465.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9886.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure 1 presents the carbon emissions per capita and per unit GDP for Shaanxi Province from 2010 to 2018. The per capita carbon emissions show a continuous increase from 21.76 to 29.04 t/person. Compared with those in 2010, the per capita carbon emissions of construction land in 2018 had increased by 33.46%. Carbon emissions per unit GDP showed an overall downward trend from 0.072 to 0.040 kg/yuan. The carbon emissions per unit GDP for construction land had dropped by 44.44% in 2018 compared with that in 2010; before 2014, the decline was large, whereas after 2014, the decline began to slow down.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSpatial differences in carbon emissions from construction lands in all cities and the demonstration area\u003c/h2\u003e \u003cp\u003eIn 2010 and 2018, the carbon emissions from construction lands in Shaanxi Province were the largest in Yulin, at 1810.67 and 2412.25 t, respectively. In 2010, the order of carbon emissions from construction land was as follows: Yulin\u0026thinsp;\u0026gt;\u0026thinsp;Xi\u0026rsquo;an \u0026gt;\u0026thinsp;Weinan\u0026thinsp;\u0026gt;\u0026thinsp;Xianyang\u0026thinsp;\u0026gt;\u0026thinsp;Baoji\u0026thinsp;\u0026gt;\u0026thinsp;Hanzhong\u0026thinsp;\u0026gt;\u0026thinsp;Yan\u0026rsquo;an \u0026gt;\u0026thinsp;Tongchuan\u0026thinsp;\u0026gt;\u0026thinsp;Ankang\u0026thinsp;\u0026gt;\u0026thinsp;Shangluo\u0026thinsp;\u0026gt;\u0026thinsp;Yangling Demonstration Area. In 2018, the order was Yulin\u0026thinsp;\u0026gt;\u0026thinsp;Xi\u0026rsquo;an \u0026gt;\u0026thinsp;Weinan\u0026thinsp;\u0026gt;\u0026thinsp;Baoji\u0026thinsp;\u0026gt;\u0026thinsp;Xianyang\u0026thinsp;\u0026gt;\u0026thinsp;Hanzhong\u0026thinsp;\u0026gt;\u0026thinsp;Yan\u0026rsquo;an \u0026gt;\u0026thinsp;Tongchuan\u0026thinsp;\u0026gt;\u0026thinsp;Ankang\u0026thinsp;\u0026gt;\u0026thinsp;Shangluo\u0026thinsp;\u0026gt;\u0026thinsp;Yangling Demonstration Area. Carbon emissions continued to increase owing to sustained development as a result of urbanization and industrialization. Among the cities and the demonstration area in Shaanxi Province, Yulin produced the highest carbon emissions from construction land, which increased from 1810.67 t in 2010 to 2412.25 t in 2018, with an average annual increase of 4.16 t. In contrast, the lowest emissions were from Yangling Demonstration Area; they increased from 31.66 t in 2010 to 41.07 t in 2018, with an average annual increase of 4.95 t. In 2018, Yulin and Xi\u0026rsquo;an produced carbon emissions greater than 2000 t; Weinan, Baoji, Xianyang, Hanzhong, and Yan\u0026rsquo;an produced emissions greater than 500 t; and Tongchuan, Ankang, Shangluo, and Yangling Demonstration Area produced emissions less than 500 t. The Yangling Demonstration Area produced less than 50 t of carbon emissions. In 2010, Yulin, Xi\u0026rsquo;an, and Weinan produced carbon emissions greater than 1,000 t; Baoji, Xianyang, and Hanzhong produced emissions greater than 500 t; and Tongchuan, Ankang, Shangluo, and Yangling Demonstration Areas produced emissions less than 500 t. The Yangling Demonstration Area again produced less than 50 t of carbon emissions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCarbon emissions from construction lands in Shaanxi Province in 2010 and 2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdministrative District\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCarbon emissions\u003c/p\u003e \u003cp\u003e(t)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePer capita carbon emissions\u003c/p\u003e \u003cp\u003e(t/person)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePer unit GDP carbon emissions\u003c/p\u003e \u003cp\u003e(kg/Yuan)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2010\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2018\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2010\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2018\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2010\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2018\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXi\u0026rsquo;an\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1594.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2185.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTongchuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e248.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaoji\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e591.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e794.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXianyang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e600.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e779.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeinan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1139.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1517.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYan\u0026rsquo;an\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e419.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e548.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHanzhong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e503.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e707.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1810.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2412.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnkang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e221.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e308.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShangluo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYangling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere were obvious regional differences in carbon emissions per capita and per unit GDP in Shaanxi Province. In 2010 and 2018, the per capita carbon emissions from Yulin, at 5.40 and 7.06 t, respectively, were significantly higher than those from other cities. In 2010, the order of per capita carbon emissions was Yulin\u0026thinsp;\u0026gt;\u0026thinsp;Tongchuan\u0026thinsp;\u0026gt;\u0026thinsp;Weinan\u0026thinsp;\u0026gt;\u0026thinsp;Yan\u0026rsquo;an \u0026gt;\u0026thinsp;Xi\u0026rsquo;an \u0026gt;\u0026thinsp;Baoji\u0026thinsp;\u0026gt;\u0026thinsp;Yangling Demonstration Area\u0026thinsp;\u0026gt;\u0026thinsp;Hanzhong\u0026thinsp;\u0026gt;\u0026thinsp;Xianyang\u0026thinsp;\u0026gt;\u0026thinsp;Ankang\u0026thinsp;\u0026gt;\u0026thinsp;Shangluo. In 2018, the order was basically the same as that in 2010. The per capita carbon emissions in 2010 were greater than 5 t for Yulin; greater than 2 t for Tongchuan and Weinan; greater than 1 t for Yan\u0026rsquo;an, Xi\u0026rsquo;an, Baoji, Yangling Demonstration Area, Hanzhong, and Xianyang; and less than 2 t for Ankang and Shangluo. In 2018, the per capita carbon emissions were greater than 7 t for Yulin; greater than 2 t for Tongchuan, Weinan, Yan\u0026rsquo;an, Xi\u0026rsquo;an, Baoji, and Hanzhong; greater than 1 t for Yangling Demonstration Area, Xianyang, and Ankang; and less than 1 t for Shangluo. In 2010, the largest and smallest carbon emissions per unit GDP in Shaanxi Province were exhibited by Weinan (0.142 kg/yuan) and Xi\u0026rsquo;an (0.049 kg/yuan), respectively. The order of carbon emissions per unit GDP was as follows: Weinan\u0026thinsp;\u0026gt;\u0026thinsp;Tongchuan\u0026thinsp;\u0026gt;\u0026thinsp;Yulin\u0026thinsp;\u0026gt;\u0026thinsp;Hanzhong\u0026thinsp;\u0026gt;\u0026thinsp;Ankang\u0026thinsp;\u0026gt;\u0026thinsp;Yangling\u0026thinsp;\u0026gt;\u0026thinsp;Baoji\u0026thinsp;\u0026gt;\u0026thinsp;Shangluo\u0026thinsp;\u0026gt;\u0026thinsp;Xianyang\u0026thinsp;\u0026gt;\u0026thinsp;Xi\u0026rsquo;an \u0026gt;\u0026thinsp;Yan\u0026rsquo;an. In 2018, the order of per capita carbon emissions was Tongchuan\u0026thinsp;\u0026gt;\u0026thinsp;Weinan\u0026thinsp;\u0026gt;\u0026thinsp;Yulin\u0026thinsp;\u0026gt;\u0026thinsp;Hanzhong\u0026thinsp;\u0026gt;\u0026thinsp;Baoji\u0026thinsp;=\u0026thinsp;Yan\u0026rsquo;an \u0026gt;\u0026thinsp;Shangluo\u0026thinsp;\u0026gt;\u0026thinsp;Ankang\u0026thinsp;=\u0026thinsp;Yangling Demonstration Area\u0026thinsp;\u0026gt;\u0026thinsp;Xi\u0026rsquo;an (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eConstruction land carbon emission intensity and prediction model\u003c/h2\u003e \u003cp\u003eConstruction land area and carbon emissions increased in Shaanxi Province from 2010 to 2018. Compared with that in 2010, the construction land area in 2018 had increased by 18.46% (Fig.\u0026nbsp;2). We found that there was an exponential correlation between the construction land area and carbon emissions; the correlation coefficient was 0.9248 (Fig.\u0026nbsp;3). The fitted function can be used as a model for predicting carbon emissions in the future and can provide a scientific basis for quantitative accounting of carbon emissions from construction land.\u003c/p\u003e \u003cp\u003eThe carbon emission intensity of construction land can be expressed as carbon emissions per unit construction land area. From 2010 to 2018, the carbon emission intensity of construction lands in Shaanxi Province showed an increasing trend. Compared with that in 2010, the carbon emission intensity of construction land in 2018 had increased by 13.85% (Fig.\u0026nbsp;4). The regional differences in carbon emission intensity of construction lands in Shaanxi Province were obvious. In 2010 and 2018, Yulin exhibited the highest carbon emission intensity, at 21.99 and 19.55 t/hm\u003csup\u003e2\u003c/sup\u003e, respectively; Yangling Demonstration Area exhibited the lowest carbon emission intensity, at 0.34 and 0.37 t/hm\u003csup\u003e2\u003c/sup\u003e, respectively. In 2010, the carbon emission intensity was greater than 15 t/hm\u003csup\u003e2\u003c/sup\u003e in Yulin and Xi\u0026rsquo;an, greater than 10 t/hm\u003csup\u003e2\u003c/sup\u003e in Weinan, greater than 5 t/hm\u003csup\u003e2\u003c/sup\u003e in Xianyang, Baoji, and Hanzhong, and lesser than 5 t/hm\u003csup\u003e2\u003c/sup\u003e in Yan\u0026rsquo;an, Tongchuan, Ankang, Shangluo, and Yangling Demonstration Area. In 2018, the carbon emission intensity was greater than 15 t/hm\u003csup\u003e2\u003c/sup\u003e in Yulin and Xi\u0026rsquo;an, greater than 10 t/hm\u003csup\u003e2\u003c/sup\u003e in Weinan, greater than 5 t/hm\u003csup\u003e2\u003c/sup\u003e in Baoji, Xianyang, Hanzhong, and Yan\u0026rsquo;an, and lesser than 5 t/hm\u003csup\u003e2\u003c/sup\u003e in Tongchuan, Ankang, Shangluo, and Yangling Demonstration Area (Fig.\u0026nbsp;5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEcological compensation standard for carbon emissions\u003c/h2\u003e \u003cp\u003eThe ecological compensation quotas of all the cities and the demonstration area in Shaanxi Province for 2010 and 2018 were obtained using the carbon emissions from construction lands and three types of carbon sequestration prices (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEcological compensation quotas for construction lands in Shaanxi Province for 2010 and 2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdministrative District\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal carbon emissions\u003c/p\u003e \u003cp\u003e(\u0026times; 10\u003csup\u003e4\u003c/sup\u003e t)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLower limit (\u0026times; 10\u003csup\u003e8\u003c/sup\u003e Yuan)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModerate amount (\u0026times; 10\u003csup\u003e8\u003c/sup\u003e Yuan)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eUpper limit\u003c/p\u003e \u003cp\u003e(\u0026times; 10\u003csup\u003e8\u003c/sup\u003e Yuan)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2010\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2018\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2010\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2018\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2010\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2018\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2010\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2018\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXi\u0026rsquo;an\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1594.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2185.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e163.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e224.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTongchuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e248.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaoji\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e591.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e794.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e81.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXianyang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e600.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e779.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e61.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e79.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeinan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1139.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1517.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e116.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e155.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYan\u0026rsquo;an\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e419.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e548.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e43.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e56.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHanzhong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e503.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e707.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e72.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1810.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2412.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e185.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e247.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnkang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e221.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e308.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e31.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShangluo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYangling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eYulin should provide the highest ecological compensation for 2018, with an amount ranging from 29.43 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e to 247.50 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e Yuan, followed by Xi\u0026rsquo;an, Weinan, and Baoji. Based on the average cost of afforestation in China, the latter three cities should provide ecological compensations of 59.6 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e, 41.37 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e, and 21.67 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e Yuan, respectively. Yangling Demonstration Area should provide the lowest ecological compensation, with an amount ranging from 0.5 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e to 4.21 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e Yuan. The order of ecological compensation standards for all the cities and the demonstration area in Shaanxi Province was basically the same for 2010 and 2018. The moderate amounts of ecological compensation that should be provided by all the cities and the demonstration area in Shaanxi Province for 2018 is 1.29 to 1.44 times that for 2010. Among the administrative districts, Yangling Demonstration Area and Tongchuan exhibit the smallest and largest growth multiples, respectively. The growth periods were the shortest and longest for Yangling Demonstration Area and Tongchuan, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eFrom 2010 to 2018, carbon emissions from construction lands in Shaanxi Province increased but carbon emissions per unit GDP decreased overall. Owing to continuous economic development, the demand for construction land increased, and in turn increased carbon emissions. However, due to the development of national high technology, the energy consumption per unit GDP decreased. These findings were confirmed in this study. Yulin\u0026rsquo;s industry is relatively developed, and hence carbon emissions are large. Energy consumption per unit GDP has been the lowest in Xi\u0026rsquo;an. This is closely related to Xi\u0026rsquo;an\u0026rsquo;s high-tech development, emphasis on the use of eco-friendly energy, and positioning as an eco-tourism city. China should develop new products, technologies, and equipment, reduce carbon emissions per unit GDP, and implement national ecological protection policies.\u003c/p\u003e \u003cp\u003eAt this stage, the ecological compensation market in China is not perfect, and the compensation mechanism is not sound. Few regions implement ecological compensation\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. It is basically still in the initial development stage. Compensation for returning farmland to forests, which is the rudimentary form of ecological compensation, is relatively mature. It is recommended to use the afforestation cost method to determine such compensation. Internationally, Germany started ecological compensation first, in the 1960s. The scope of ecological compensation has gradually expanded to land occupied by construction projects. Any loss of water resources, air quality, flora and fauna, and landscapes and other ecological environments needs to be compensated. The types of compensation have been gradually diversified and include in-situ compensation, relocation, payment, and combinations of these. The most important objective is to give full play to the market mechanism, establish a mature trading market for the ecological compensation index, and use the market to adjust the price of ecological compensation\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. China can learn from the advanced experience of Germany and make efforts to improve ecological compensation from the perspectives of system, policy formulation, loss assessment, research on compensation methods, and improvement of compensation markets.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e(1) From 2010 to 2018, the total and per capita carbon emissions from construction lands in the cities and the demonstration area of Shaanxi Province showed an upward trend. Compared with those in 2010, the total and per capita carbon emissions from construction land in Shaanxi Province in 2018 had increased by 34.86% and 33.46%, respectively. Carbon emissions per unit GDP showed a downward trend overall. Compared with those in 2010, carbon emissions per unit GDP in 2018 had dropped by 44.44%. Before 2014, the decline was large, whereas after 2014, the decline began to slow down.\u003c/p\u003e \u003cp\u003e(2) There were obvious regional differences in the total, per capita, and per unit GDP carbon emissions from construction land in Shaanxi Province. In 2010 and 2018, the carbon emissions from construction land in Shaanxi Province were the largest in Yulin and the smallest in Yangling Demonstration Area; the per capita carbon emissions in Yulin were significantly higher than those in other urban areas. In 2010, the carbon emissions per unit GDP were the largest and smallest in Weinan and Xi\u0026rsquo;an, respectively.\u003c/p\u003e \u003cp\u003e(3) The construction land area in Shaanxi Province correlated with the carbon emissions from 2010 to 2018, and the correlation coefficient was 0.9248. The fitted function can be used as a model for predicting carbon emissions and can provide a scientific basis for quantitative accounting of carbon emissions from construction land. Compared with that in 2010, the carbon emission intensity of construction land in 2018 had increased by 13.85%; regional differences in carbon emission intensity were obvious in Shaanxi Province. In 2010 and 2018, the carbon emission intensity of Yulin was the highest whereas that of Yangling Demonstration Area was the lowest.\u003c/p\u003e \u003cp\u003e(4) For 2018, Yulin should provide the highest ecological compensation, whereas Yangling Demonstration Zone should provide the lowest. The order of ecological compensation standards for all the cities and the demonstration area in Shaanxi Province are basically the same for 2010 and 2018. The moderate amount of ecological compensation that should be provided by all the cities and the demonstration area in Shaanxi Province for 2018 is 1.29 to 1.44 times that for 2010. The growth period was shortest for Yangling Demonstration Zone and longest for Tongchuan.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by This work was Supported by the Fundamental Research Funds for the Central Universities, CHD (300102352503); Shaanxi Provincial Land Engineering Construction Group fund (DJNY 2021-29); Natural Science Basic Research Program of Shaanxi (2021JQ-961).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003estatement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.N. wrote the main manuscript, L.N. and Z.Y. conceived the study, experimentation, Z.Y. revised manuscript and figures and L.J. analyzed the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eJ. 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China Land, (5), 49-51 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Carbon emissions, construction land, ecological compensation, Shaanxi ","lastPublishedDoi":"10.21203/rs.3.rs-2161826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2161826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConstruction lands are the main sources of carbon emissions. In this study, data on the energy consumption, permanent population, and gross domestic product (GDP) of Shaanxi Province from 2010 to 2018 were collected. Using a carbon emission assessment model, the emissions from all the cities and the demonstration area in Shaanxi Province were evaluated. Ecological compensation standards for carbon emissions were determined. The analyses showed the following results: (1) From 2010 to 2018, the total and per capita carbon emissions from construction land showed an upward trend. Generally, the carbon emissions per unit GDP for all the cities and the demonstration area in Shaanxi Province showed a downward trend. (2) The total, per capita, and per unit GDP regional carbon emissions in Shaanxi Province varied significantly. In 2010 and 2018, Yulin and Yangling Demonstration Area showed the highest and lowest total carbon emissions, respectively. Yulin and Shangluo showed the highest and lowest per capita carbon emissions, respectively. In 2010, the highest and lowest carbon emissions per unit GDP were from Weinan and Xi’an, respectively, whereas in 2018, they were from Tongchuan and Xi’an, respectively. (3) The construction land area correlates with the carbon emissions from Shaanxi Province between 2010 and 2018, and the correlation coefficient is 0.9248. The fitted function can be used as a model for predicting carbon emissions and can provide a scientific basis for quantitative accounting of carbon emissions from construction land. (4) According to moderate estimates, the ecological compensation that should be provided by all the cities and the demonstration area in Shaanxi Province for 2018 is 1.29 to 1.44 times that for 2010. The growth periods were the shortest and longest for Yangling Demonstration Area and Tongchuan, respectively. These results can act as a reference to plan low-carbon, green, and sustainable economic development in Shaanxi Province.\u003c/p\u003e","manuscriptTitle":"Research on Ecological Compensation for Construction Land from a Carbon Emission Perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-19 14:14:45","doi":"10.21203/rs.3.rs-2161826/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc25ccee-dcc3-4d65-b2b5-1323ef9008f0","owner":[],"postedDate":"October 19th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":16283314,"name":"Biological sciences/Ecology"},{"id":16283315,"name":"Biological sciences/Evolution"},{"id":16283316,"name":"Earth and environmental sciences/Ecology"}],"tags":[],"updatedAt":"2022-11-02T07:29:26+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-19 14:14:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2161826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2161826","identity":"rs-2161826","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}