Land use, total carbon emissions change and low carbon land management in Coastal Jiangsu, China

Journal of Cleaner Production xxx (2014) 1e10

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Land use, total carbon emissions change and low carbon land management in Coastal Jiangsu, China Xiaowei Chuai a, *, Xianjin Huang a, b, **, Wanjing Wang c, Rongqin Zhao d, Mei Zhang a, Changyan Wu a a

School of Geographic & Oceanographic Sciences, Nanjing University, Nanjing 210023, Jiangsu Province, China Land Development and Consolidation Technology Engineering Center of Jiangsu Province, Nanjing 210023, Jiangsu Province, China College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China d North China University of Water Resources and Electric Power Zhengzhou, Henan 450011, Henan Province, China b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 28 August 2013 Received in revised form 31 January 2014 Accepted 8 March 2014 Available online xxx

Given the background of global warming, carbon emission reduction has become a topic of global importance. Land use change not only influences carbon storage in terrestrial ecosystems directly, but it also indirectly affects anthropogenic carbon emissions, which occur more frequently in coastal regions. Based on data of energy consumption, industrial products, waste, soil organic carbon, and vegetation, together with land use images of five typical years, this paper calculated the total carbon emissions in coastal Jiangsu, China, assigned the detailed carbon emission items to different land use types, and optimized land use structure to low carbon emissions using the Linear Programming Model. It was found that carbon emission intensity in coastal Jiangsu was much higher than the average for China as a whole, and that energy consumption contributed most to local carbon emissions with the contribution from animals second. Urban land accounted for the most concentrated and highest intensity of carbon emissions. Between 1985 and 2010, the transfer of cropland to built-up land accounted for the largest percentage of the total transferred area and contributed most to the increase of carbon emissions. In particular, the limitation of urban land will play a key role in carbon emission reduction. Our optimized land use structure can control and decrease carbon emissions effectively and thus, it is an important tool worth the consideration of land managers and policy makers. Ó 2014 Elsevier Ltd. All rights reserved.

Keywords: Carbon emissions Low carbon Land use change Economic development Land use structure optimization Coastal Jiangsu

1. Introduction The problem of global warming worsens continuously and has brought significant damage (Wallace et al., 2014). Carbon emission in China is ranked the highest in the world and it is increasing rapidly (Chuai et al., 2012; Mu et al., 2013), which has attracted the attention of the rest of the world. Under the global imperative for the development of low carbon economies, China is under great pressure to reduce carbon emissions (Chuai et al., 2012). According to the greenhouse gas inventory of the Intergovernmental Panel on Climate Change (IPCC), sources of total carbon emissions are usually divided into four sectors: 1) energy consumption, 2) industrial processes, 3) forestry, agriculture, land use, land use/cover change

* Corresponding author. Tel.: þ86 25 83596620. ** Corresponding author. School of Geographic & Oceanographic Sciences, Nanjing University, Nanjing 210023, Jiangsu Province, China. E-mail addresses: [email protected] (X.W. Chuai), [email protected] (X.J. Huang).

(LUCC), and 4) waste. The IPCC method provides detailed carbon emission coefficients, and according to the amount and type of energy consumption, waste, and industrial production, the amount of carbon emissions can be calculated. It provides a simple and practical method for the calculation of large-scale carbon emissions and it has been used widely throughout the world. In China, most of the studies have been performed with regard to carbon emissions from energy consumption, because this accounts for about 90% of China’s total carbon emissions (Lai, 2010). Many of the studies focused on national (Chang, 2010; Mu et al., 2013; Zhou et al., 2013) and provincial scales (Zhang and Wang, 2013; Chuai et al., 2012; Zhang and Huang, 2012). However, carbon sources and sinks of terrestrial ecosystems have also been active areas of research in China with investigations undertaken in single ecosystems, such as forests (Wang et al., 2013), and wetlands (Zhang et al., 2013). It has been recognized that land use change can significantly affect carbon storage because of the different capacities of differing land use types to accumulate carbon (Jaiarree et al., 2011; Bailis and McCarthy, 2011); something that has caused obvious carbon emissions from

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Please cite this article in press as: Chuai, X., et al., Land use, total carbon emissions change and low carbon land management in Coastal Jiangsu, China, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.03.046

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X. Chuai et al. / Journal of Cleaner Production xxx (2014) 1e10

both vegetation and soil (Houghton, 2008; Xie et al., 2007; Van Minnen et al., 2009). China still lacks the comprehensive carbon emissions inventory proposed by the IPCC. However, our research group selected five typical years from the 1980s to 2009 and completed a carbon emissions inventory on a provincial scale according to the IPCC’s method to make it comparable internationally (Lai, 2010). Furthermore, we attempted to extend it to local scales as a guide to carbon emissions for local government (Zhao, 2011). The effects of human activity, for example, carbon emissions, differ greatly depending on land use type and land surface characteristics and functions. For example, industrial energy consumption is concentrated on the industrial land use type, but the scale of the industrial land use may greatly affect the carbon emissions from industrial energy consumption (Lai, 2010). Human activity can affect regional carbon emissions through changes of land use patterns, which in turn alter energy consumption patterns and ultimately, influence the amount and rate of carbon emissions (Lan et al., 2012). Although anthropogenic carbon emissions are often deemed derived from social and economic factors, such as the growth of both the economy and population (Dhakal, 2009), they are always reflected in the extent of the expansion of built-up land. Therefore, we can say that social and economic factors, land use change, and carbon emissions are interactional, and that changes in total carbon emissions can be reflected by land use change. Thus, we can conclude that land use change not only causes carbon emissions from terrestrial ecosystems directly, but it also indirectly affects anthropogenic carbon emissions. Therefore, land use change might have an even more profound influence on total carbon emissions, although carbon emissions might not change linearly according to land use change. Similar results were obtained following a study by Zhao (2011) in Nanjing, which showed that land use change clearly influences carbon emissions, and other researchers have used this method to analyze both the carbon emissions characteristics of different land types and the influence of land use change (Shi et al., 2012). Further research has investigated reducing carbon emissions by optimizing land use structure; however, the studies only discussed carbon in terrestrial ecosystems (Tang et al., 2009; Zhong et al., 2006), and the influence of anthropogenic carbon emissions was only discussed superficially (Chuai et al., 2013). It has been reported that 60% of the world’s population is concentrated in coastal areas and that coastal economies develop quickly, which means that land use change is frequent (Kurt, 2013), especially for urban expansion (Ellis et al., 2011). Land use in coastal regions usually involves marine characteristics, such as shallows and salt pans distributed along the coastline and thus, total carbon emission brought by LUCC will be more obvious than in inland areas and may present distinctive coastal characteristics. Under policies introduced in 2006, most coastal provinces in China have drawn up their coastal development plans and had them approved by the Chinese Government. Jiangsu Province legalized its development plan in 2009 (Chuai, 2013) and land use has changed obviously in recent years (He, 2011; Yao, 2013). Overall, for local coastal regions, we lack the IPCC-guided examination of total carbon emissions. Land use changes frequently, but we lack analyses both of specific land use types and of detailed temporospatial total carbon emissions caused by land transitions. Quantitative research into assigning anthropogenic carbon emissions to different land use types is still at the initial stages, and there has been no research into optimizing land use structure for reducing total carbon emissions. In our study, all of these questions will be answered, such that our research will provide a meaningful resource that will aid land managers and policy makers implement the coastal development strategy in Jiangsu Province. The objectives of our study include: 1) the examination of total carbon

emissions, mainly by the method of the IPCC; 2) assigning carbon emission items to different land use types; 3) the examination of temporospatial land transition and its effect on total carbon emissions; and 4) the optimization of land use structure and a discussion of its effect on reducing carbon emissions. 2. Materials and methods 2.1. Study area The coastal region of Jiangsu has the Yellow Sea to the east and the Yangtze River to the south. The study area includes the cities of Lianyungang, Yancheng, and Nantong (Fig. 1), encompassing an area of 3.3  104 km2 between latitudes 31410 Ne35 070 N and longitudes 118 240 Ee121550 E, which accounts for 35% of Jiangsu’s entire land area. The total length of the coastline is 954 km. Most of the study area presents plain landforms with low hills located mainly to the north of Lianyungang. Soil types include aquic soil, paddy soil, and coastal saline soil. Cultivated land accounts for more than 70% of the entire region, but the percentages of woodland and grassland are much lower, and in recent years, because of rapid economic development, land use has changed frequently. 2.2. Data sources Information sources used in this paper include images of land use type from 1985 (comprising several images from the late 1980s), 1995, 2000, 2005, and 2010, and data of energy consumption, amount of waste, population, gross domestic product (GDP), yields of principal crops, livestock, industrial production, soil organic carbon, forest inventory, and other empirical data related to values of carbon densities and carbon sinks for different terrestrial ecosystems. The 100  100 m land use grid was produced from Landsat TM images. Here, we reclassify its land use types as cropland, woodland, grassland, water area, shallows, urban land, rural residential land, and other built-up land (including salt pan, which accounts for more than 70%, transportation land, and independent industrial and mine land). Energy consumption data were obtained from the China Energy Statistical Yearbook, and data on the amount of waste were derived from the Jiangsu Environmental Statistical Yearbook. Population, GDP, yields of principal crops, livestock, and industrial production data were obtained from the Statistical Yearbooks of Lianyungang, Yancheng, and Nantong, and the Statistical Yearbook of Jiangsu Province. Soil organic carbon data include two periods: the 1980s and 2000s. Data from the 1980s were derived from The

Fig. 1. Location of study area.

Please cite this article in press as: Chuai, X., et al., Land use, total carbon emissions change and low carbon land management in Coastal Jiangsu, China, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.03.046

X. Chuai et al. / Journal of Cleaner Production xxx (2014) 1e10

i, and Vindustryi represents the emission factor in the process of product i. Industrial production in coastal Jiangsu that causes carbon emissions includes steel, glass, cement, aluminum, and synthetic ammonia.

Second National Soil Survey, whereas data from the 2000s were provided by the multi-purpose regional geo-chemical survey in Jiangsu Province from 2003 to 2007, which includes 7797 soil samples, evenly distributed on a 1  1 km grid, from locations within our study area. For each soil sample, its latitude, longitude, soil organic carbon content (%), soil type, and bulk density were recorded. Forest data were obtained from The Fifth Forest Resource Inventory in Jiangsu Province. Other empirical data were obtained from previously published related research.

(3) Carbon emissions from waste Waste includes both waste water and garbage. Waste water releases carbon in form of CH4, which is determined mainly by the chemical oxygen demand (COD) in the water body. The disposal of garbage includes burial and burning, releasing carbon in the form of CH4 and CO2, respectively. The calculation of their respective carbon emissions is shown below: Carbon emissions from waste water:

2.3. Methods 2.3.1. Carbon emissions According to the IPCC, carbon emissions can be attributed to four sectors: 1) energy consumption, 2) industrial processes, 3) waste, and 4) forestry, agriculture, land use and LUCC. Owing to local characteristics, the assignation of carbon emissions to the four sectors within our framework may not totally agree with that of the IPCC. The detailed calculation process is shown below.

Cwater ¼ QCOD  VCOD  12=16

(3)

where Cwater is the amount of carbon emissions from waste water, QCOD is the amount of COD in the waste water, and VCOD is the ability to release CH4 (0.25) (IPCC, 2006). Carbon emissions from buried garbage:

(1) Carbon emissions from energy consumption The China Energy Statistical Yearbook can provide detailed data on energy consumption (Table 1). We calculated the carbon emissions from energy consumption in coastal Jiangsu according to the GDP percentage and the amount of energy consumption per GDP. The carbon estimation from energy consumption was calculated using Equation (1):


 Cenergyi ¼ Qenergyi  Kenenrgyi  VCO2 i þ VCH4 i

3

. Cwastebury ¼ Qwastebury  0:167  ð1  71:5%Þ  12 16

(4)

where Cwaste-bury is the amount of carbon emissions from buried garbage, Qwaste-bury is the amount of buried garbage, 0.167 is the emission factor of CH4 from buried garbage (IPCC, 2006), and 71.5% is the rate of water content in the buried garbage (Zhao, 2011). Carbon emission from burned garbage:

(1)

where Cenergyi is the amount of carbon emitted by energy i, Qenergyi is the amount of energy consumption of type i, Kenenrgyi is the per unit calorific value of energy i provided by the IPCC (2006), and VCO2 i and VCH4 i are the carbon emission factors of CO2 and CH4 from energy i, respectively, which were all quoted from the IPCC (2006).

Cwasteburn ¼ Qwasteburn  Vwaste  Pwaste  12=44

(5)

where Cwaste-burn is the amount of carbon emissions from burned garbage, Qwasteburn is the amount of burned garbage, Vwaste is carbon content in garbage (50%), and Pwaste is the content of mineral carbon in garbage (40%) (Zhao, 2011).

(2) Carbon emissions from industrial process (4) Carbon emissions from forestry, agriculture, land use, and LUCC

Carbon emissions from industrial processes is non-combustion emissions, which can be determined by multiplying the quantity of the product by the production-based emission factor (IPCC, 2006), as in Equation (2) (Sugar et al., 2012):

Cindustryi ¼ Q industryi  Vindustryi

According to local characteristics, the forest area in coastal Jiangsu is limited and its carbon emissions can be neglected. Livestock is well developed with high population density and thus, carbon emissions from their respiration, enteric fermentation, and feces are worth considering. Therefore, we add animals to our framework when examining the total carbon emissions. Animals raised in coastal Jiangsu mainly include cattle, pigs, sheep, donkeys,

(2)

where Cindustryi represents the carbon emissions amount from industrial production i, Qindustryi is the amount from production Table 1 Assignment of carbon emission items to different land use types. Land use types

Energy consumption

Industrial process

Waste

Forestry, agriculture, land use and LUCC Land use and LUCC

Animals

Cropland Woodland Grassland Water area Shallows Urban land

Agriculture Forestry e Fishery, water conservancy e Industry, construction, wholesale retail trade and catering services, urban residential consumption Rural residential consumption, graziery

e e e e e Industrial production

e e e e e Waste water and solid garbage from industry and urban household

Soil Vegetation Vegetation Soil Vegetation Vegetation

e e e e e Urban population

e

Waste water from rural household

Vegetation and soil

Industry, transportation, storage and postal and telecommunications

e

e

Soil

Rural residential land Other built-up land

and soil and soil and soil and soil

Rural population and animals e

Please cite this article in press as: Chuai, X., et al., Land use, total carbon emissions change and low carbon land management in Coastal Jiangsu, China, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.03.046

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X. Chuai et al. / Journal of Cleaner Production xxx (2014) 1e10

and poultry, and their contribution to carbon emissions is calculated below: Carbon emissions from animal respiration:

Cbreathei ¼ Ni  Vbreathei

(6)

where Cbreathei is the amount of carbon emissions from respiration of animal i, Ni is quantity of animal i, and Vbreathei is the carbon emissions coefficient from the respiration of animal i (Zhao, 2011). Carbon emissions from animal enteric fermentation and feces:

Ci ¼ Ni  ðV1i þ V2i Þ

(7)

where Ci is the amount of carbon emissions from enteric fermentation and feces of animal i, Ni is the quantity of animal i, and V1i and V2i are the carbon emission coefficients from enteric fermentation and feces of animal i, respectively (Zhao, 2011). Carbon emissions from land use and LUCC. Carbon emissions caused by land use and LUCC refers to emissions from terrestrial ecosystems (vegetation and soil), here we only considered carbon emissions caused by land use type changes, but not including the effect from land use management, which can be calculated as follows:

Cveg ¼

X

Rvegi  Areavegi

(8)

Rsoili  Areasoili

(9)

graziery energy consumption were assigned to rural residential land. Carbon emissions from humans were assigned to urban land and rural residential land according to their relative population densities. Carbon emissions from land use and LUCC were from carbon sink/source changes of vegetation and soil. Because crops have shorter growing seasons compared with forests and grasslands and because straw is mainly returned to the soil and burned, its carbon sink is always deemed zero (Fang et al., 2007); therefore, we only consider carbon emissions or absorption from the soil of cropland. Vegetation in urban land and rural residential land was calculated by greening rate, and the carbon sink value was determined by the mean carbon sink value of forest and grassland, which are the main green types of urban and rural residential land. 2.3.3. Total carbon emissions change caused by land use change Carbon emission intensity varies significantly among different land use types, and land use transition will cause carbon emissions change. Therefore, carbon emissions here, not only includes carbon change in terrestrial ecosystems caused by LUCC, as discussed above, but also includes anthropogenic carbon emissions, for which land is its carrier (Equation (10)):

C ¼

n
X

 Vi  Vj  Stransferij

(10)

ij

i

Csoil ¼

X i

where Cveg and Csoil are the carbon emissions from vegetation and soil, respectively, Rveg-i is the carbon sink value of different vegetation types on different land use types, which was quoted from the study of Lai (2010). Rsoil-i is the soil carbon sink/source value under different land use types, which is examined by our soil sample data, Areaveg-i and Areasoil-i is the area of different land use types, respectively. Owing to the lack of soil sample data in the soils of shallows and under water bodies, the soil carbon sink/source values quoted here were obtained from the study of Duan et al. (2008). They confirmed that the soils of shallows and under water bodies in nearby regions play a role of carbon sink with values of 23.6 and 56.7 t km2 a1, respectively.

2.3.2. Assignment of carbon emissions to different land use types The China Energy Statistical Yearbook can provide detailed energy consumption data and we assign these items to different land use types according to the actual distribution of activities (Table 1). Carbon emissions from industry are distributed both on urban land (industrial land) and other built-up land (independent industrial and mining land) and thus, we assign the carbon emissions according to their outputs. Industrial products discussed in our study were those finished in factories in the urban region and therefore, we classify their carbon emissions to urban land. Waste is mainly from industry and households in the forms of waste water and solid garbage, for which urban and rural residential land are attributed as its carriers. However, different from urban land, rural residential land has little industry to produce waste water and solid garbage, and solid garbage is always discarded without disposal (burning or burial; the two main methods to produce carbon emissions); therefore, waste water from households is considered the main carbon emissions source. For urban land, the main carbon emissions sources of waste not only include waste water, but also include solid garbage through the burial or burning of waste from both industry and urban households. As animals in coastal Jiangsu are usually reared in pens, carbon emissions from animals and

where C represents the total carbon emissions change caused by land use change, Vi and Vj are carbon emission intensities of land use type i and j, and Stransferij represents the area of land use transition from type j to i. To determine the type of land transition that has a decisive effect on total carbon emissions, we created a transition matrix of land use using intersect analysis in ArcGIS9.3, and we used the mean values of carbon intensities for the different land use types of the five typical years to describe the carbon intensities between 1985 and 2010. According to the land use transition matrix and the mean values of carbon emission intensity for the different land use types, the average annual carbon emissions transfer between 1985 and 2010 can be calculated. 2.3.4. Land use structure optimization We used the Linear Programming Model to optimize land use structure. This includes establishing the target function and establishing constraint conditions. LINGO software (version 10.0) was used to complete the optimization.

T ¼

n X

Ai Vi ¼ min

i ¼ 1; 2; 3; .; n

(11)

i¼1

where T is the regional total carbon emissions (t), Ai is the area of land use type i (km2) and Vi is the carbon emission intensity of land use type i (t.km2), which can be established from the total carbon emissions assigned to land use type i and its area Ai. Among other things, the constraint conditions depend on local land use policies, economic and social development plans, and special planning related to land use. This paper established constraint conditions for eight variables as follows: cropland X1, woodland X2, grassland X3, water area X4, shallows X5, urban land X6, rural residential land X7, and other built-up land X8. Here, we define 2010 as the initial year and assign 2020 as our target prediction year. Owing to the effect of sediment accumulation, the coastal area of Jiangsu is expanding, and according to the rate of expansion between 2005 and 2010, we predict the total area could reach 33,513.14 km2 by 2020. Therefore, the first constraint condition can be established as:

Please cite this article in press as: Chuai, X., et al., Land use, total carbon emissions change and low carbon land management in Coastal Jiangsu, China, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.03.046

X. Chuai et al. / Journal of Cleaner Production xxx (2014) 1e10 8 X

Xi ¼ 33; 513:14

Xi  0

(12)

i¼1

The area of cropland has decreased rapidly in recent years. Under the scenario of increasing carbon storage, cropland will become strictly protected and the rapid rate of decrease will be controlled. However, considering the fast local development, the decreasing trend will not change. Based on food demand, 21,925.19 km2 of cropland can guarantee local self-sufficiency in food (this area was predicted by local government); however, here, we consider this the lower value. With the development of urbanization, the rural population will continue to decrease and the idle rural residential land could be changed into cultivated land through land consolidation, which will reduce the fast of decrease. According to the rate of decrease between 2005 and 2010 and the land consolidation potential of rural residential land, we calculate that the total area will be 23,730.64 km2 in 2020. Because consolidation of rural residential land will undergo some pressure and will take a long time, we consider 23730.64 km2 as the upper value.

21; 925:19  X1  23; 730:64

(13)

Woodland has the greatest capability to accumulate carbon and therefore, woodland should be strictly protected and the rate of area decrease stopped. Thus, its area in 2020 should be higher than that in 2010 (311.3 km2), and according to the 12th Five-Year Forestry Development Planning made by local government and the demands of ecological construction, the area of woodland will increase by 85.51 km2 by 2020 compared with 2010 if implemented well. Therefore, the constraint conditions are established as:

311:3  X2  396:81

(14)

Grassland is mainly distributed in Yancheng. Its total area has been decreasing year upon year and it had shrunk to 520.47 km2 in 2010. Because of its limited area and the demand to protect grass ecological environments, the area of coastal grassland will increase in the future. According to the Yancheng City Coastal Agricultural Development Planning, 333.33 km2 of grass will be planted in coastal Yancheng. However, considering the state of the economy and the development of tourism, this target appears too ambitious. We assume that 50e80% of the target 333.33 km2 will be achieved by 2020. Therefore, the forecast for this area in 2020 allows us to develop the constraint conditions as follows:

687:14  X3  787:13

(15)

Because of the development of aquaculture, water area grew quickly during the period 2005e2010 (595.74 km2), and according to that growth rate, it will reach 2896.35 km2 by 2020. Through adjustments of the industrial structure, the demand for aquaculture will shrink in the future, and its area will be transferred mainly to grassland, as discussed above, such that the actual water area by 2020 will be much lower than 2896.35 km2. However, the water area in our land classification includes land for water conservancy facilities, and this land use type will increase by 94.3 km2 compared with 2010, according to the water conservancy development planning of the three coastal cities, which will mean that the total water area will be 2394.9 km2. We consider this as the lower value and therefore, the constraint conditions are established as:

2394:9  X4  2896:35

5

previously. We also considered the grassland plan made by local government. The calculated results are as shown below:

846:32  X5  946:32

(17)

4855:88  X6 þ X7 þ X8  5274:49

(18)

With rapid economic development and urbanization, the increasing trend of built-up land is difficult to change, according to the population and per capita living space, and the anticipated demand for traffic land, at least 4855.88 km2 of built-up land will be required by 2020, which we consider as the lower value. The higher value is obtained from land use planning in Jiangsu Province (5274.49 km2), which considered mainly the demands of economic development. The constraint conditions of built-up land are established as:

Urban land will keep increasing to feed the economic development. Without control, its area will reach 2360.59 km2 by 2020, according to the rate of increase between 2005 and 2010. However, according to the standards of per capita living space, specified in the urban planning of China, coastal Jiangsu exceeds the requirements of modern city construction. Therefore, we predict 1439.96 km2 by 2020 with the medium level of 100 m2 per capita, which can be deemed as the lower value. The constraint conditions of urban land are established as:

1439:96  X6  2360:59

(19)

With the decrease of the rural population, rural residential land will decrease inevitably in the future, and the redundant land can be transferred to cropland by land consolidation. However, this may take a long time because of the traditional rural way of life in China. In the local land plan report, 344 km2 of rural residential land is planned for consolidation between 2010 and 2020. We assume that this target will be achieved and thus, we consider the area of 1931.9 km2 as the lower value and the existing area in 2010 as the higher value.

1931:9  X7  2275:9

(20)

The prediction of other built-up land was made according to local plans for traffic land and independent industrial land and mining land (Chuai, 2013). The constraint conditions are given as:

1484:02  X8  1609:7

(21)

3. Results 3.1. Carbon emissions inventory in typical years As indicated in Fig. 2, total carbon emissions in coastal Jiangsu amounted to 822.17  104 t in 1985 and increased to 2931.52  104 t

(16)

Shallow land was converted mainly to water area and grassland, which we called the lost area, and due to sediment accumulation effects, it has gained area from the sea year upon year, as discussed

Fig. 2. Carbon emissions inventory in typical years of Coastal Jiangsu (104 t).

Please cite this article in press as: Chuai, X., et al., Land use, total carbon emissions change and low carbon land management in Coastal Jiangsu, China, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.03.046

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in 2010, which is an increase of 2.57 times and reflects an accelerating growth of emissions during the 25 years. Land use and LUCC (terrestrial ecosystems) played a relatively stable role in absorbing carbon with a capacity that increased from 62.45  104 t in 1985 to 72.15  104 t in 2010; this can offset 8% of the total carbon emissions in 1985, but only 2.5% in 2010. Energy consumption contributed the most to total carbon emissions in every typical year. Its percentage increased from 51.68% in 1985 to 72.62% in 2010, and it grew rapidly between 2005 and 2010. Animals (including humans) were the second largest source of emissions in coastal Jiangsu, presenting an increasing trend with amounts rising from 364.43  104 t in 1985 to 522.33  104 t in 2010, but the overall contribution percentage decreased from 44.33% to 17.82% due to the rapid increase of energy consumption. Carbon emissions from industrial processes was much lower than from energy consumption and animals, but it increased the most rapidly from 19.42  104 t to 262.47  104 t between 1985 and 2010. Carbon emissions from waste accounted for the least and a relatively stable amount; it increased from 13.39  104 t in 1985 to 17.82  104 t in 2010. Overall, the net amount of carbon emissions in coastal Jiangsu was 759.72  104 t in 1985, which increased to 2859.37  104 t in 2010. Energy consumption and animals are the two main sources of carbon emissions and both, especially energy consumption, increased significantly between 1985 and 2010. Carbon absorption by terrestrial ecosystems did not change much and its function to offset carbon emissions appeared weak.

accounted for most carbon emissions and it increased most rapidly; its carbon emissions in 2010 accounted for 1686.76  104 t, which was more than five times that of 1985, and accounted for 59% of the total carbon emissions. Carbon emissions from rural residential land and other built-up land both increased from 350.51  104 t to 540.93  104 t and from 123.19  104 t to 637.34  104 t, respectively. In 2010, other built-up land began produce more carbon emissions than rural residential land, which indicates that carbon emissions from other built-up land grew more quickly. Determined by both land area and amount of carbon emissions, the intensity of carbon emissions may fluctuate in different years. Table 2 shows that the average carbon emissions density kept increasing from 244.11 t km2 a1 in 1985 to 856.65 t km2 a1 in 2010. The carbon emissions intensity of urban land ranged between 8007.72 and 19,417.04 t km2 a1, which is much higher than all the other land use types. Before 2005, rural residential land had higher carbon emission intensity than other built-up land, but it was much lower in 2010. Of all the land use types, the carbon emissions intensity of other built-up land increased most obviously with carbon emissions intensity in 2010 more than four times that of 1985. For shallows and grassland areas, as they had no carbon emission from anthropogenic activities, their carbon emissions intensities were constant, determined by soil and vegetation types. Carbon emissions intensity of woodland decreased throughout and decreased sharply from 35.39 to 13.81 t km2 a1 between 2005 and 2010. Carbon emissions intensities from cropland and water areas presented much lower and fluctuating values.

3.2. Carbon emissions from different land use types According to the relationship between land use types and the detailed carbon emissions, we examined the amount of carbon emissions from different land use types and their intensity per unit area (Table 2). Cropland, woodland, and shallows areas all played a role of carbon sink in all of the five typical years. Water area played a role of carbon sink in 1985 and as carbon source in all four of the other typical years because of the increase of energy consumption in fishery and water conservancy. Although we do not assign carbon emissions to grassland from energy consumption, it always played a role of weak carbon source, which is determined by the carbon released from soil; however, it showed that this decreased slowly from 0.31  104 t to 0.12  104 t. As for built-up land, urban land Table 2 Amount and intensity of carbon emissions on different land use types. Year

1985

1995

2000

2005

2010

Land use types Cropland Woodland Grassland Water area Shallows Urban land Rural residential land Other built-up land Total

Amount of carbon emissions (10 t) 9.10 0.64 5.76 10.97 1.56 1.37 1.43 1.29 0.31 0.30 0.22 0.21 5.70 2.23 2.26 2.44 3.53 3.53 3.89 3.76 305.60 491.57 515.52 929.96 350.51 403.62 440.34 517.83 123.19 181.43 224.15 363.09 759.72 1073.60 1171.39 1797.51

3.11 0.43 0.12 0.81 3.05 1686.76 540.93 637.34 2859.37

Land use types Cropland Woodland Grassland Water area Shallows Urban land Rural residential land Other built-up land Average

Intensity of carbon emissions (t km2 a1) 3.62 0.26 2.31 4.40 1.28 43.68 38.23 39.48 35.39 13.81 2.30 2.30 2.30 2.30 2.30 36.58 14.37 13.27 14.15 3.52 26.53 26.53 26.53 26.53 26.53 8007.72 11,191.63 10,972.15 19,417.04 15,239.83 1864.97 2018.71 2186.62 2444.37 2376.77 1063.48 1556.66 1708.13 2708.47 4590.46 244.11 331.23 354.98 541.86 856.65

4

Note: The positive number represents carbon emissions and the negative is carbon absorption.

3.3. Land use transition and average annual total carbon emissions variation caused by land use change A land use transition matrix between 1985 and 2010 was created using intersect analysis in ArcGIS9.3; however, the expansion of the total area was not considered in this section. Table 3 shows that the land use type of 3861.94 km2 of land changed, which accounted for 11.71% of the entire study area. This caused carbon emissions to increase by 41.57  104 t annually compared with the land use structure of 1985 (assuming no land transition occurred between 1985 and 2010). The annual emissions changes discussed below were all compared with the assumption of no land transition. The roll-out of cropland, woodland, grassland, water area, shallows, and rural residential land all caused the average annual carbon emissions to increase during 1985 and 2010, whereas the roll-out of urban land and other built-up land both acted to reduce the average annual carbon emissions. The roll-out of cropland was mainly occupied by built-up land, comprising 1415.29 km2, which accounted for 87.66% of the entire roll-out area, especially to rural residential and urban land with areas of 715.45 and 611.53 km2, respectively. The roll-out of cropland increased annual carbon emissions by 38.79  104 t between 1985 and 2010. This is much higher than the roll-out of other land use types and was caused by land transition to urban land with the amount of 31.72  104 t. 75.68% of grassland had been transferred to other land use types. This meant that the absolute area amounted to 1007.54 km2, which was the second largest roll-out area after cropland. However, as it was transferred mainly to cropland and water area, it only increased the annual carbon emissions by 1.8  104 t, and mainly concentrated on the transfer of grassland to other built-up land (1.7  104 t). The transferred area of woodland was only 71.92 km2, which increased annual carbon emissions by 0.56  104 t. The transferred 206.54 km2 of water area caused an annual increase in carbon emissions of 1.4  104 t, which was caused mainly by the transition to urban land and other built-up land. The roll-out of shallows areas only increased annual carbon emissions by 0.37  104 t, because of the low transferred area of

Please cite this article in press as: Chuai, X., et al., Land use, total carbon emissions change and low carbon land management in Coastal Jiangsu, China, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.03.046

X. Chuai et al. / Journal of Cleaner Production xxx (2014) 1e10

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Table 3 Land use transition matrix and annual emissions change during 1985e2010 in coastal Jiangsu. 1985

2010 Cropland

Woodland

Grassland

21.23 283.33 1.22 1.30 0.34 0.56 2.00 0.54 310.52

Annual carbon emissions transition (t/a) Cropland 0.00 26.96 Woodland 68.91 0.00 Grassland 58.07 1.78 Water area 16.49 1.86 Shallows 0.55 0.10 Urban land 5825.25 291.20 Rural residential land 22,520.13 176.99 Other built-up land 4470.33 50.97 Total 32,820.81 549.86

Land use transfer (km2) Cropland Woodland Grassland Water area Shallows Urban land Rural residential land Other built-up land Total

23,520.72 54.27 310.61 100.07 0.57 11.23 258.18 48.01 24,303.66

Water area

Shallows

Urban land

8.70 0.42 323.72 11.04 157.22 0.56 0.47 6.29 508.42

168.83 2.30 472.15 1336.80 183.90 4.01 11.56 82.19 2261.78

0.50 0.04 34.97 18.12 808.24 0.02 0.13 7.19 869.21

611.53 9.62 1.31 16.75 0.00 356.55 104.18 6.34 1106.28

715.45 2.23 4.23 16.95 0.06 33.90 1497.60 2.68 2273.14

88.32 3.04 183.05 42.31 34.97 1.36 2.92 1004.40 1360.32

25,135.30 355.25 1331.26 1543.38 1185.30 408.19 1877.08 1157.59 32,993.30

1.63 0.61 0.00 0.24 181.31 290.38 40.91 584.50 732.00

27.80 3.30 10.46 0.00 208.00 2079.41 1006.43 7639.38 10,496.59

0.48 0.01 40.33 20.49 0.00 10.39 11.47 676.43 759.58

317,213.92 5002.32 679.28 8685.83 0.00 0.00 44,953.19 2698.36 379,232.91

62,406.07 197.35 368.18 1475.70 5.29 14,627.69 0.00 15.77 49,809.12

8223.69 286.92 17,010.03 3932.62 3289.94 578.83 17.19 0.00 32,181.55

387,845.65 5559.43 17,946.85 14,055.54 3684.98 23,703.15 21,214.45 10,739.02 415,864.70

377.06 km2, which was transferred mainly to water area and grassland. Rural residential land was mainly consolidated to cropland and occupied by urban land, which caused the annual carbon emissions to decrease by 2.25  104 t and increase by 4.5  104 t, respectively. The effect of reducing carbon emissions was most obvious in response to the roll-out of urban land, where the lowest transferred area of 51.64 km2 brought a reduction of annual carbon emissions of 2.37  104 t between 1985 and 2010, which is determined by its high carbon emissions intensity. The transition of other built-up land caused a reduction of 1.07  104 t in annual carbon emissions, which was contributed mainly by the transitions to cropland and water area. We also produced maps of the main land use transfers (Fig. 3) and the changes in average carbon emission intensity (Fig. 4) between 1985 and 2010 to facilitate spatial land management. Fig. 3 shows that land transition was concentrated near the coastline area, especially near Yancheng and Nantong. Transition types included mainly grassland to cropland and water area, and shallows to grassland and water area, which always resulted in the lowering of the carbon emission intensity (Fig. 4). Land transitions in inland areas presented a scattered distribution. The main transition types included cropland to urban land, rural residential land, and water area. The distribution of the transition of cropland to urban land is relatively concentrated, always occurring near areas in which cities are located, whereas transitions of cropland to rural residential land and water area are scattered throughout the region; these transitions always cause an increase in carbon emission intensity. Overall, the transfer of cropland to built-up land accounted for the largest percentage of the total transferred area and contributed the most to increase carbon emission. The roll-out of urban land and other built-up land can reduce carbon emission effectively. The area near the coastline exhibits high rates of land transition concentrated distributions. These transitions always result in reducing carbon emission intensity, whereas inland areas present a relatively scattered distribution of land transitions, which always act to increase carbon emission intensity.

Rural residential land

Other built-up land

Total

considered land demand to feed urbanization and economic development, in which the changing trends of different land use types were nearly the same as between 2005 and 2010 (Chuai, 2013). The other was called the “low carbon scenario” as optimized above. This considered not only the land demand to feed urbanization and economic development, but it also aimed to reduce carbon emissions. Using carbon emissions intensity data from 2010, the quantity of carbon emissions under both scenarios of land use structure were calculated and compared with carbon emissions under the land use structure of 2010 (Table 4).

3.4. Effect of optimized land use structure We designed two development scenarios to predict land use structure in 2020. One was called the “natural scenario” that only

Fig. 3. Main land use transfers between 1985 and 2010.

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4. Discussion

Fig. 4. Average carbon emissions intensity changes brought by main land use transfer between 1985 and 2010 (t km2 a1).

Table 4 shows that by 2020, under the low carbon scenario, we could reduce carbon emissions by 1542.96  104 t compared with the natural scenario. This is a reduction rate of 31.66%. The limitation of urban land expansion contributes the most, reducing carbon emissions by 1403.02  104 t, which accounts for 90.9% of the entire reduction. Rural residential land and other built-up land will contribute 81.76  104 t and 57.69  104 t, respectively. The reduction of other land use types is limited, but at least the trend of increase in carbon emissions from their land area change will be altered. Carbon emissions under our optimized land use structure is a little higher than under the land use structure of 2010. However, the increase in carbon emissions of 470.58  104 t shows that the increasing trend has been controlled well compared with the period of 2000e2010. The increased carbon emissions will be brought about mainly by urban land expansion and therefore, we can conclude that the limitation of built-up land plays a key role in reducing carbon emissions, especially for urban land.

In our study, we define land as the carrier for carbon emissions and assume that carbon emissions activities on certain land surfaces will increase or decrease proportionally according to land use change, which may not be entirely consistent with reality. The yearly variation of carbon sink attributed to vegetation was also not considered because of its weak influence on total carbon emissions. Therefore, the only changes in carbon emissions that we considered are those resulting from the conversion of one land use type to another. Our study region accounted for about 0.35% of the entire area of China, whereas its carbon emissions accounted for 2.33%, 1.44%, 1.48%, 1.16%, and 1.11% of China’s total carbon emissions calculated using a consistent method for five typical years (Lai, 2010). This indicated that carbon emissions intensity in coastal Jiangsu was 6.66, 4.11, 4.21, 3.32, and 3.16 times that of the average level for China as a whole. However, the rate of increase of carbon emissions deviated from the average level and gradually became lower. Economic development is always the primary influencing factor behind carbon emissions (Acaravci and Ozturk, 2010; Zhang and Cheng, 2009). The high carbon emissions intensity in coastal Jiangsu can be explained by the advanced economic level. The gradually lowering rate of increase of carbon emissions was affected by the adjustment of the industrial structure, especially with regard to the development of coastal tourism (Wang and Zou, 2005). Another local characteristic is the significant carbon emissions from animals, which made the percentage contribution from energy consumption much lower than that for China as a whole. Urban land accumulated most carbon emissions, which is consistent with other studies. This is due to mainly human activities related to high carbon emissions, which always makes urban land a carrier (Huang et al., 2013; Zhao and Huang, 2010). Thus, the limitation of urban land plays a key role in reducing local carbon emissions, especially for industrial land (Lai, 2010). The average annual transfer of land caused carbon emissions to increase by 41.57  104 t annually, compared with the land use structure in 1985 (assuming that no land transition occurred between 1985 and 2010). This accounted for about 3% of the average annual carbon emissions from 1985 to 2010 (Chuai, 2013). This percentage is not high, but because the average annual transfer of land accounted for only 0.4% of the total area, carbon emissions caused by land use change in coastal Jiangsu are obvious, which highlights the importance of the adjustment of land use structure proposed in our study. Owing to the high carbon emissions intensity of built-up land, we must strictly limit its expansion and protect cropland. The near-coastline area has the highest possibility to experience land use change. This is determined by the development policy towards the coastal zone

Table 4 Examination of the effect of the optimized low carbon land use structure. Land use types

Cropland Woodland Grassland Water area Shallows Urban land Rural residential land Other built-up land Total

Land use structure (km2)

Carbon emissions under different land use structures (104 t)

2010

Natural scenario in 2020

Low carbon scenario in 2020

2010

Natural scenario in 2020

Low carbon scenario in 2020

24,325.31 311.30 520.47 2300.61 1149.66 1106.81 2275.90 1388.40 33,378.46

23,094.09 311.30 687.14 2394.90 779.52 2360.59 2275.90 1609.70 33,513.14

23,730.64 396.81 787.13 2796.35 946.32 1439.96 1931.90 1484.02 33,513.14

3.11 0.43 0.12 0.81 3.05 1686.76 540.93 637.34 2859.37

2.95 0.43 0.16 0.84 2.07 3597.50 540.93 738.93 4872.91

3.03 0.55 0.18 0.98 2.51 2194.48 459.17 681.23 3329.95

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X. Chuai et al. / Journal of Cleaner Production xxx (2014) 1e10

proposed by the provincial government. Therefore, the nearcoastline area is the region that requires most attention. Transition types from grassland and shallows in the near-coastline area should also be limited to reduce carbon emissions, and we should consider protection of the ecological environment during the process of coastal exploitation (Shen, 2011). Because of the decrease of the rural population, the demand for rural residential land will diminish. Therefore, rural residential land has high probability to be converted to cultivated land and other types of land use. As this presents the second largest contributor to reduce carbon emissions, we should strengthen rural land consolidation. Our optimized land use structure can reduce carbon emissions by 31.66% compared with the natural development scenario. The essence of the plan is to limit the area of land with high carbon emissions intensity and to increase those land areas with low carbon emissions intensity, based on meeting local economic development. Thus, our optimized land use structure can effectively control the sharply decreasing trend of ecological land use types (such as cultivated land, woodland, and grassland) and can limit the expansion of built-up land. Furthermore, ecological lands can also provide benefits other than carbon storage, such as water conservation and flood control, erosion control, fuel, food, biodiversity and other (Konarska et al., 2002). Therefore, although the carbon emissions intensities for different land uses may not be the same in 2020 as in 2010, the optimized structure presents an effective scheme that is worth consideration. Furthermore, even though the constraining conditions in the Linear Programming Model may include some uncertainties due to changes of governmental development policies, adjustments of industrial structure, and technological improvements, we can readjust the model to accommodate the new policies, and economic and social demands, such that the efficacy and value of the method is preserved. Owing to the relatively lower level of economic development, land use optimization potentialities to reduce carbon emissions may be much higher in the vast inland areas of China (Chuai et al., 2013); thus, our method could be applied throughout China to help reduce national carbon emissions. If local governments pay sufficient attention and adopt detailed actions to support it, such as limiting urban expansion and the access conditions of industrial enterprises, developing intensive land use, strengthening the protection of ecological lands, implementing related administrative strategies, and assigning tasks to specific departments to guarantee implementation, we are sure that our suggestion could reduce carbon emissions and help local governments complete their carbon emissions tasks assigned by the central government of China. Because of regional differences, local governments could adopt different actions depending on the actual local situation. 5. Conclusions Given the background of global warming, carbon emissions reduction has become a topic of global importance. Land use change brought about by human activities is a principal driver of changes to terrestrial ecosystem carbon storage (Su et al., 2006; Jiao et al., 2010). Furthermore, human activities related to anthropogenic carbon emissions always make land a carrier (Huang et al., 2013). Land use change not only influences carbon storage in terrestrial ecosystems directly, but it also indirectly affects anthropogenic carbon emissions (Lan et al., 2012). Coastal regions usually have highly developed economies, which drive frequent changes of land use (Yao, 2013). Based on data of energy consumption, industrial products, waste, soil organic carbon, vegetation, and maps of land use of five typical years, this paper completed the carbon emissions inventory guided by the IPCC and

9

local characteristics of coastal Jiangsu, China. Furthermore, it assigned detailed carbon emission items to different land use types, established the land use and average annual carbon emissions transition matrix between 1985 and 2010, produced maps of the main land use transfers (Fig. 3) and changes of average carbon emissions intensity (Fig. 4), and optimized the land use structure using the Linear Programming Model to reduce carbon emissions. The aim of this paper is to explore the relationship between land use patterns and total carbon emissions, to analyze the effect on carbon emissions brought about by land use change, and to propose a feasible and effective method for land managers and policy makers to consider ways in which to reduce carbon emissions. Our research indicates that total carbon emissions in coastal Jiangsu amounted to 822.17  104 t in 1985 and increased to 2931.52  104 t in 2010, which represents an increase of 2.57 times from 1985 to 2010. Energy consumption and animals are the two principal sources of carbon emissions, both of which increased significantly between 1985 and 2010. Carbon emissions intensity in coastal Jiangsu was much higher than the average of China as a whole. Urban land concentrated most carbon emissions and increased most rapidly; its carbon emissions in 2010 amounted to 1686.76  104 t, which was more than five times that of 1985, and accounted for 59% of the total carbon emissions. Carbon emissions from rural residential land and other built-up land increased between 1985 and 2010 from 350.51  104 t to 540.93  104 t and from 123.19  104 t to 637.34  104 t, respectively. In 2010, other built-up land began to concentrate more carbon emissions than rural residential land, reflecting that carbon emissions from other built-up land grew much more quickly. Between 1985 and 2010, the transfer of cropland to built-up land accounted for the largest percentage of the total transferred area, and contributed the most to the increase in carbon emissions. The roll-out of urban land and other built-up land can reduce carbon emissions effectively. The near-coastline area has a high rate of land transition and presents a concentrated distribution, but always resulting in the reduction of carbon emissions intensity. Inland areas present a relatively scattered distribution, but always causing an increase in carbon emissions intensity. Our optimized land use structure can reduce carbon emissions in 2020 by 1542.96  104 t, compared with the natural scenario. This is a reduction rate of 31.66%, and it is only a little higher than under the land use structure of 2010; thus, showing that the increasing trend has been controlled well. The limitation of urban land expansion is the most effective control, reducing carbon emissions by 1403.02  104 t, which accounts for 90.9% of the entire reduction. Therefore, the limitation of urban expansion will play a key role in carbon emissions reduction. In summary, carbon emissions intensity in coastal Jiangsu was much higher than the average of China as a whole. Energy consumption contributed the most to local carbon emissions, and animals were the second largest source of carbon emissions. Urban land concentrated most of the carbon emissions and therefore, the limiting urban land expansion will play a crucial role in the reduction of carbon emissions. Despite the simplifying assumptions made, the optimized land use structure not only meets the land use demands for economic and social development, but also decreases carbon emissions effectively and thus, will be of great value to land managers and policy makers. Acknowledgments This research was supported by The Clean development mechanism (CDM) projects of China (No. 1214073 and No. 2012065), and The National Natural Science Foundation of China (No. 41301633).

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Please cite this article in press as: Chuai, X., et al., Land use, total carbon emissions change and low carbon land management in Coastal Jiangsu, China, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.03.046