Land use structure optimization based on carbon storage in several regional terrestrial ecosystems across China

environmental science & policy 25 (2013) 50–61

Available online at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/envsci

Land use structure optimization based on carbon storage in several regional terrestrial ecosystems across China Xiaowei Chuai a, Xianjin Huang a,b,*, Li Lai a,c, Wanjing Wang d, Jiawen Peng a, Rongqin Zhao a,e a

School of Geographic & Oceanographic Sciences, Nanjing University, Nanjing 210093, Jiangsu Province, China Land Development and Consolidation Technology Engineering Center of Jiangsu Province, Nanjing 210093, Jiangsu Province, China c Jiangsu Information Center, Nanjing 210013, Jiangsu Province, China d College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China e College of Resources and Environment, North China Institute of Water Conservancy and Hydroelectric Power, Zhengzhou 450011, Henan Province, China b

abstract article info Land use change is a main driver of carbon storage in terrestrial ecosystems. Based on land Published on line 6 November 2012

use data, research results related to carbon densities in vegetation and soil as well as government policies related to development in different regions of China, this paper

Keywords:

optimized land use structure in 2020 for different regions with the goal of increasing

Carbon density

terrestrial ecosystem carbon storage. We defined seven types of land use: (1) cultivated

Carbon storage

land, (2) garden land, (3) woodland, (4) pasture land, (5) other agricultural land, (6) urbanized

Land use

land, and (7) a mixture of other land which we call mixed land which included open water,

Structure optimization

swamps, glaciers and other land as defined below. We found: (1) For most eastern regions,

Terrestrial ecosystem

woodland has the highest carbon (C) densities while C densities of pasture land and

Urbanization

cultivated land did not differ widely. Both have C densities higher than urbanized land while urbanized land has higher carbon densities than the areas placed in the mixed land type. (2) Under an optimized land use structure projected for 2020, the area of cultivated land will decrease compared with 2005 for most regions. The areas of garden land, pasture land and other agricultural land are much smaller compared with the mixed land use type, and the changes there are not obvious and their contributions to increased carbon storage are not significant. The area of woodland will increase the most obviously and it will contribute the most to increased carbon storage. The increasing urbanization of land and the decreasing trend of other land types make it difficult to change carbon storage patterns since the Chinese economy is expanding rapidly. (3) The optimized land use structure presented here will have effects on the entire country though with regional differences. Some inland regions will always have a larger potential to increase carbon storage than other areas because the potentialities in some coastal regions are limited by social and economic development. # 2012 Elsevier Ltd. All rights reserved.

* Corresponding author at: School of Geographic & Oceanographic Sciences, Nanjing University, Nanjing 210093, Jiangsu Province, China. Tel.: +86 25 83596620. E-mail address: [email protected] (X.J. Huang). 1462-9011/$ – see front matter # 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.envsci.2012.05.005

environmental science & policy 25 (2013) 50–61

1.

Introduction

Changes in how and where carbon is stored in terrestrial ecosystems are very important to global carbon circulation and global warming (IPCC, 2000). Batjes (1996) reported total global carbon storage in terrestrial ecosystems can reach to 2000–2500 Pg (1 Pg = 1015 g), which includes 500–600 Pg from global vegetation and 1500–1900 Pg from the upper 1 m of soil. Terrestrial ecosystems absorbed 0.9 Pg/a carbon which accounted for 12.5% of carbon emissions from energy consumption and cement production during 2000–2005 (Lal, 1999). Land use change caused by human activities is a main driver to terrestrial ecosystem carbon storage (Su et al., 2006; Jiao et al., 2010). First, it can change the vegetation cover which can directly influence vegetation biomass. For example, the changes from woodland to other land types, and especially the changes to urbanized land, will greatly reduce vegetation biomass and will release carbon into the atmosphere (Fang et al., 2007; Houghton, 2008; Bailis and McCarthy, 2011), while changes from other land use types to woodlands can always increase its vegetation biomass and carbon storage (Zhao et al., 2011; Chuai et al., 2011a,b, 2012). Second, land use changes also have a profound influence on soil organic carbon (Bushchbacher et al., 1988; Fu et al., 1999a,b; Solomon et al., 2000; Kong et al., 2009; Jaiarree et al., 2011). Soils can be a source or sink for atmospheric carbon depending on land use and management techniques (Lal, 2002; Umakant et al., 2010). Barnett et al. (2005) reported that, in the past 20 years, about one fourth of anthropogenic carbon dioxide emissions are caused by land use changes, especially deforestation, and the rest is caused by fossil fuel burning. Long-term experimental studies have confirmed soil organic carbon is highly sensitive to land use changes since native ecosystems, such as forest or grassland, become agricultural systems, resulting in the loss of organic carbon (Paul et al., 1997). With the development of urbanization, more and more farmland, woodland and grassland has been converted to urbanized land; this results in the land area being converted from carbon sink to carbon source, as a large amount of carbon is released from these disturbed terrestrial ecosystems into atmosphere (Deng et al., 2009). If one compares emissions from the combustion of fossil fuels with changes in emissions resulting from land use change, the mechanisms involved in carbon emissions caused by land use change are more complex and poorly known. The impact on carbon balance caused by land use and land cover change in terrestrial ecosystems has become the focus of global change research in recent decades (Houghton, 2002, 2003). In response to global climate change, many plans have been made to reduce carbon emissions around the world (Wang et al., 2010), such as the use of biofuels, carbon capture and storage planning for energy use in industrial areas (Fargione et al., 2008; Searchinger et al., 2008; Tilman et al., 2009; Melillo et al., 2009; Service, 2009) and the plan to reduce carbon emissions from deforestation and degradation (REDD) made by agriculture and forestry departments (Chu, 2009; Haszeldine, 2009). Most of these plans face considerable difficulties created by a technological problems as well as the business and politics of energy use (Stuart, 2009), so reductions

51

in carbon emissions are often difficult to achieve. Using current technology, enhancing the function of land-based carbon sinks through adjusting land use patterns and management seems to be an effective plan which can be used to increase carbon storage in terrestrial ecosystems (IPCC, 2005; Yu and Wu, 2011). Research related to land use structure optimization based on low carbon emissions is still in its infancy, although some scholars have made tentative studies in local areas. Zhong et al. (2006) optimized land use readjustment to reduce carbon storage loss in Cuiyuan Village in Hubei province, China. Tang et al. (2009) optimized land use structure in Tongyu County, Jilin Province, China to maximize carbon storage in terrestrial ecosystems, and found the plan effective in reducing carbon emissions. Yu and Wu (2011) designed the Land Use Structure Optimization of Low-carbon Dynamic Control Model in a study of Taixing City, Jiangsu Province, China and found it can meet the requirements of maximizing the efficiency of land resource allocation and sustainable development. Since China faces great pressure to reduce its portion of the world’s carbon emission (Chuai et al., 2012), making low-carbon land use structure optimization plans for different regions of China seems very necessary and meaningful. We used land use data and data related to carbon densities of vegetation along with soil organic carbon data from the top 1 m of soil as well as developmental policies of different regions in China to study the carbon densities for different land use types and establish a land use structure optimization model for different regions of China.

2.

Methods

2.1.

Data sources

Data used in this paper includes a China land use type map from the 1980s, a provincial administrative zoning map produced by Chinese government, provincial land use structure data from 2005, planned provincial land use for 2020, and a map of vegetation density and soil organic carbon density. The 1 km  1 km grid land use map of China from the 1980s was obtained from the Modis image of Landsat TM (Lai, 2009). The Chinese provincial administrative zoning map was provided by the National Geomatics Center of China. Land use structure data in 2005 and the planned provincial land use for 2020 were provided by the China Land Surveying and Planning Institute. This paper did not include data from Taiwan, Hong Kong and the Macao Special Administrative Regions because of a lack of data from these areas. Carbon densities of different vegetation types were obtained using mean values from existing research in China. Many scholars have conducted research related to vegetation carbon densities in different regions of China. Lai (2009) collected data from more than 800 related research projects in recent years which can cover almost all kinds of vegetation found in China. Mean carbon densities of 50 different vegetation types have been summarized. Using comprehensive analysis (Lai, 2009) and the China Vegetation Map compiled in the 1980s, we produced distribution maps

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environmental science & policy 25 (2013) 50–61

Fig. 1 – Distribution of vegetation carbon densities map in China (kg/m2).

showing different vegetation carbon densities map in China, and according to this produced map and land use type map we can calculate vegetation carbon densities of different land uses. Since if the land use types remained the same from the 1980s to 2000s, the distribution of their vegetation covers did not change significantly, for example, although the area of woodland in one district may changed form 1980s to 2000s, vegetation types in the unchanged woodland areas were

almost the same, so the changes of vegetation carbon densities of different land uses between 1980s and 2000s were not visible, the old Vegetation Map compiled in the 1980s can well be used here (Fig. 1). The most recent soil data covering all of China were data from the Second National Soil Survey of China (National Soil Survey Office, 1996) conducted in the 1980s. Soil organic carbon (SOC) data used in this paper were also obtained from

Fig. 2 – Distribution of SOC densities map in China (kg/m2).

environmental science & policy 25 (2013) 50–61

that national survey. Lai (2009) made a summary and calculated SOC densities for 68 different soil types in China. Based on the previous studies (Lai, 2009) and the China Soil Type Map in China, we produced a distribution map of SOC densities in China (Fig. 2).

2.2.

53

constraint conditions, LINGO software (version 10.0) was used to finish the optimization following the methods of Lai (2009). T¼

n X Ai ðVi þ Si Þ ¼ max i ¼ 1; 2; 3; . . . ; n

(2)

i¼1

Carbon densities and storage of different land uses

The meaning of T, Ai, Vi and Si are the same as in Formula (1).

ArcGIS9.3 software was used to create an intersection analysis from three different maps, the China provincial administrative zoning map, the 1 km  1 km land use map of China in the 1980s and Fig. 1. With this new map, mean vegetation carbon density values of different land uses can be calculated for each region. With the same method, average SOC density values of different land uses can also be calculated for each region. So, we can calculate total carbon storage in the terrestrial ecosystem for each region (Formula (1)): T¼

n n X X Ti ¼ Ai ðVi þ Si Þ i¼1

i ¼ 1; 2; 3; . . . ; n

(1)

i¼1

where T is regional total carbon storage of the terrestrial ecosystem, Ti is carbon storage of land use type i, Ai is the area of land use type i, and Vi and Si are carbon densities of vegetation and soil of land use type i, respectively. The land classification system has been readjusted based on The Second National Land Survey completed during 2007– 2009, and compared with the old land classification system made by Chinese Academy of Sciences (Lai, 2009). Based on the new land classification system, to make a more precise analysis we divide the landscape into cultivated land, garden land, woodland, pastureland, other agricultural land, urbanized land and a mixture of other land which did not fit into the other six groups we call mixed land. Here cultivated land includes irrigated paddy land, irrigated land and dry cropland. Garden land indicates land planted with nursery or fruit trees. Woodland includes arbor forests, bamboo forests, shrubs and coastal mangroves. Other agricultural land includes ponds, roads and water treatment facilities scattered in cultivated land. The mixed land type includes rivers, lakes, beaches and natural reservation land (swamps, sandy land, saline alkali soil land, bare land, plateau wilderness and land covered by glaciers and snow. Since the 1 km  1 km land use map of China in the 1980s was made based on the old land classification system (farmland, woodland, grassland, water area, urbanized land and unused land), farmland here includes cultivated land, garden land and other agricultural land. In the new land classification system we define carbon densities of cultivated land, garden land and other agricultural land as having the same as value as farmland. The value of mixed land in the new land classification system includes water area and unused land in the old system, so we took value of water area and unused land as the carbon density for the mixed land type in new land classification system.

The constraint conditions depended on local land use policies, economic and social development plans, special planning related to land uses and so on. This paper established constraint conditions for seven variables as follows: cultivated land X1, garden land X2, woodland X3, pastureland X4, other agricultural land X5, urbanized land X6, and mixed land X7. Here we use Jiangsu Province as an example. Jiangsu Province covers 106,741 km2, so the first constraint condition can be established as: 7 X Xi ¼ 106; 741 Xi  0

The average replanted index during 1980–2005 in Jiangsu is 1.58. Grain crops accounted for 72% of this total. Based on food demand (Chuai et al., 2008), 49,617 km2 is needed for cultivation. But considering economic development, we can only guarantee a quantity of food grain needed to make the province selfsufficient. Industrial grain can be supplied by the national market. Under these conditions, the need for cultivated land is 42,078 km2, so we establish the constraint conditions as: 42; 078  X1  49; 617

(4)

The area of garden land in 2005 is 3177 km2. The extent of garden land has decreased in the past few years. Based on the known rate of decrease this will shrink to 2823 km2 in 2020, so the constraint conditions are established as: 2823  X2  3177

(5)

Based on the 12th Five-Year Plan for Jiangsu Province, forest coverage will grow to 20.3% in 2010 and 23% in 2015. Based on this planned growth rate between 2010 and 2015, forest coverage rate will reach 25.7%, so this was selected as the low value. Based on a past rate of increase for woodland from past years, forest coverage rate will reach to 26% in 2020. We took this as the high value, since the calculation of forest coverage not only includes vegetation of woodlands but also include farmland shelterbelts, partly garden land and forest distributed among other agricultural land and urbanized land. Based on land use structure in 2005, 35% of cultivated land, 6% of garden land, 25% of other agricultural land and 12% of urbanized land can contribute to the calculation of the forest coverage rate; we assume it will not change in 2020, so here we establish the constraint conditions as: 25; 550  X3 þ 0:35  X1 þ 0:06  X2 þ 0:25  X5 þ 0:12  X6  27; 753

2.3.

(3)

i¼1

(6)

Land use structure optimization

The Linear Programming Model was selected to optimize land use structure. In the development of the model, including establishing the target function (Formula (2)) and establishing

The extent of pasture land in Jiangsu Province has been decreasing year after year and this trend is difficult to change. Based on this historic decline and a slowing of this decline caused by the more recent protection to pastureland based on

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environmental science & policy 25 (2013) 50–61

rules which have been recently adopted, the forecast for this area in 2020 allows us to develop the constraint conditions as follows: 10:73  X4  21:2

(7)

The area of other agricultural land in 2005 is 13,260 km2. This increased in the past few years, and based on the current rate of increase it will reach 14,587 km2, so the constraint conditions are established as: 13; 260  X5  14; 587

(8)

We establish a regression equation using data for the area of urbanized land and population, GDP, and fixed assets investment level during 1996–2005. Based on the level of developmental growth syndicated by the variables, urbanized land area in 2020 should at least reach 19,379 km2. 19; 379  X6

(9)

Mixed land includes open water and unused land in the old land classification system. The area covered by both of them decreased slowly in recent years. Based on the historic high and low rate of decrease it is expected to decline to about 17,573 km2 to 18,266 km2, so the constraint conditions are established as: 17; 573  X7  18; 266

(10)

After finishing the constraint conditions we established a target function to maximize carbon storage (Formula (11)): maxðZÞ ¼ 9:43X1 þ 9:43X2 þ 12:73X3 þ 10:21X4 þ 9:43X5 þ 8:87X6 þ 5:37X7

(11)

Next, the optimized land use structure was determined using LINGO 10.0 software.

3.

Limitations and assumptions

Vegetation and SOC densities can be affected by many factors such as land cover, soil type, land management, climatic factors and so on. This paper only compared vegetation and SOC densities under different land uses. Vegetation biomass is the main factor used to determine carbon densities (Al-Kaisi and Grote, 2007; Steenwerth and Belina, 2008). Land covered with higher biomass vegetation always has higher vegetation and SOC densities. For example, woodland usually has higher vegetation and SOC densities compared with other land use types (Lai, 2009; Xi et al., 2008). According to the China vegetation type maps produced in the 1980s (Hou, 1982) and 2000s (Editorial Board of Vegetation Map of China, 2001), vegetation types did not change significantly from the 1980s to 2000s if land use types did not change. But other factors such as local land management and climatic conditions may change, but the changes were not significant enough to change the vegetation type and SOC densities order under different land uses. Chuai et al. (2012) compared the mean value of SOC densities under different land uses both in the 1980s and 2000s and found SOC densities could be compared as follows: woodland > pasture land > cultivated land > urbanized land (or water area) > unused land, and SOC

densities of all land uses increased from the 1980s to 2000s. Since our analysis results are mainly determined by the order of vegetation and SOC densities under different land uses, vegetation carbon densities from mean value of related studies in recent years and soil carbon densities data from the 1980s used in this paper can easily be used in our study, although these factors may affect the precision of the measurement of increased carbon storage to some extent. In our study, we assumed that none of the land-use types are either gaining or losing carbon over time, both from vegetation and soil. The only changes in carbon storage that we considered are changes resulting from the conversion of one land-use type to another. Other factors such as tillage practices, land management techniques, climatic factors, and fertilizer uses were not considered.

4.

Results

4.1.

Carbon densities of different land uses

Table 1 provides carbon densities of seven land use types for different regions in China. It shows carbon densities differed widely across different land use types and regions. Woodland always has higher carbon densities compared with other land use types for all the regions of China. This is consist with many previous studies which show forested systems always have the highest levels of carbon sequestration (Xi et al., 2008; Chuai et al., 2012). Carbon densities of pastureland are close to cultivated land, and in some regions carbon densities of cultivated land are even little higher than pastureland such as in Shanxi, Inner Mongolia, Liaoning, Jilin and so on. This contradicts earlier studies which show the conversion from pastureland to farmland will release carbon into atmosphere (Fang et al., 2007; Houghton, 2008). Carbon densities of urbanized and mixed land are relative lower in all regions of China when compared with other land use types, and urbanized land has higher carbon densities than mixed land types in most of the regions. It is worth noting the types of regions discussed here include independent cities such as Shanghai and Beijing, provinces like Jiangxi or Jiangsu, and autonomous regions.

4.2.

Optimized land use structure

We established land use constraint conditions for each province and then used LINGO 10.0 to optimize land use structure for all provinces. Then we compared the optimized land use structure in 2020 with the land use structure in 2005. Table 2 shows cultivated land will decrease by 2020 when compared with 2005 for all regions except Jilin, Jiangxi provinces and Xinjiang autonomous regions. This may be the result of rapid urbanization near the cities of China. This expansion of cities is expected to occupy a large area of cultivated land, creating extreme pressure in favor of the protection of cultivated land. The extent of garden, pasture and other agricultural land is expected to shrink rapidly compared with other land use types but this change can be adjusted considerably based on local development plans. These changes are not obvious and the contribution in carbon

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environmental science & policy 25 (2013) 50–61

Table 1 – Provincial carbon densities of different land use types in the 1980s (kg/m2). Region

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Xizang Shaanxi Gansu Qinghai Ningxia Xinjiang China

Agricultural land Cultivated land

Garden land

Woodland

Pasture land

Other agricultural land

7.94 8.11 8.37 6.56 11.90 12.01 15.18 18.39 10.88 9.43 10.52 9.15 10.44 10.63 8.47 7.69 10.53 10.57 10.55 10.29 9.94 9.18 8.80 11.22 10.33 10.34 7.53 7.57 11.73 6.09 9.07 10.44

7.94 8.11 8.37 6.56 11.90 12.01 15.18 18.39 10.88 9.43 10.52 9.15 10.44 10.63 8.47 7.69 10.53 10.57 10.55 10.29 9.94 9.18 8.80 11.22 10.33 10.34 7.53 7.57 11.73 6.09 9.07 10.44

9.93 9.01 11.64 8.16 26.09 14.47 22.99 27.32 12.14 12.73 11.89 11.88 12.44 12.43 8.99 10.83 13.91 12.89 12.89 13.69 15.64 12.53 17.08 13.27 14.72 19.05 12.76 16.44 17.75 11.54 19.71 16.54

8.80 8.60 9.10 6.02 9.60 9.24 14.58 23.52 10.54 10.21 9.83 10.25 10.16 10.25 8.13 7.35 12.09 10.86 10.33 10.14 9.78 10.30 16.40 11.09 10.95 8.96 7.89 9.12 13.14 5.68 8.31 10.15

7.94 8.11 8.37 6.56 11.90 12.01 15.18 18.39 10.88 9.43 10.52 9.15 10.44 10.63 8.47 7.69 10.53 10.57 10.55 10.29 9.94 9.18 8.80 11.22 10.33 10.34 7.53 7.57 11.73 6.09 9.07 10.44

storage is not expected to be significant. The area of woodland in the optimized land use structure in 2020 will increase the most obviously when compared with 2005, with the extent of woodlands in the entire study area increasing by 20.4734  104 km2, which equals 8.68% of the woodland area presents in 2005 period. This indicates the opportunity exists to dramatically increase forest coverage, especially in inland hilly and undeveloped districts such as Inner Mongolia autonomous regions, Shanxi, Hebei and Gansu provinces which can increase forest coverage by 3.7011  104 km2, 2.8804  104 km2, 2.0443  104 km2 and 1.8303  104 km2, respectively, and Heilongjiang, Yunnan, Liaoning, Shaanxi, Qinghai, Guizhou, Sichuan provinces and Xinjiang Autonomous Region which can increase forest coverage by 5044– 9736 km2. The opportunities to increase forest coverage in districts such as Tianjin, Shanghai, Beijing and Chongqing is limited because these areas are small and the demand is high for land used for social and economic development. Coastal areas in eastern and southern China such as Jiangsu, Hainan, Shandong, Zhejiang, Guangzhou and Fujian provinces also have limited potential to increase their woodland area, again mainly because of the demand for land for social and economic development. There are also inland districts with extensive land area, undeveloped economic conditions and expansive hilly areas, such as Hubei, Jiangxi, Anhui and Hunan provinces, but their potential to increase the extent of

Urbanized land

Mixed land

7.06 6.55 7.55 6.63 9.78 11.40 14.53 16.70 9.30 8.87 10.23 8.25 9.72 10.24 8.32 7.01 8.96 10.18 10.42 9.90 9.07 7.78 9.90 10.34 10.41 7.47 7.08 6.86 9.67 6.24 7.04 9.36

6.02 8.81 10.25 5.70 5.79 11.22 12.54 24.65 9.13 5.37 7.58 4.67 8.86 5.15 7.44 5.43 6.85 7.57 8.52 8.09 8.82 4.87 15.59 9.05 10.41 6.42 4.14 3.71 7.15 4.31 3.04 5.18

their woodland area is limited. This may be because of their special land management plans which were established at a time when less emphasis was being placed on protecting and increasing the extent of forested land. So, because forested land has higher carbon densities and the amount of land area available for forests is obviously increasing compared other land use types, woodland can contribute the most to increase carbon storage in terrestrial ecosystems, especially in inland hilly districts with undeveloped economic conditions. Once social and economic conditions begin to develop rapidly, the demand for urbanized land will continue to increase. Urbanized land areas in the optimized land use structure modeled for 2020 will increase for all regions compared with 2005, especially in Shandong, Henan and Sichuan provinces with the demand exceeding 2000 km2. As for the mixed land type, which includes many areas greatly affected by human activities, the decreasing trend is difficult to change. Some non-forested lands can be converted to urbanized land and some can be modified into farmland, but we should also take measures to protect many rare species and fragile natural environments.

4.3.

Effect of the optimized land use structure

Total carbon storage in China in 2005 was 101,664.19 Tg (1 Tg = 1012 g). Based on our model it will be 102,895.52 Tg in

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environmental science & policy 25 (2013) 50–61

Table 2 – Changes of the optimized land use structure in 2020 compared with 2005 for different regions of China (100T km2). Region Cultivated land

Garden land

Woodland

Pasture land

Other agricultural land

1.87 0.82 12.25 7.88 12.48 2.75 33.61 8.68 2.38 4.99 5.69 4.13 8.07 5.92 4.02 2.73 4.39 4.60 4.40 3.91 0.96 9.20 10.83 13.43 11.44 0.48 19.76 2.17 0.62 0.86 101.80 24.46

3.82 0.02 57.23 5.52 1.60 0.00 0.44 0.89 0.39 3.54 8.76 0.46 1.98 4.78 0.00 1.39 2.70 0.00 12.63 18.61 1.98 10.40 20.36 3.09 29.24 0.02 8.80 3.59 0.14 0.07 25.61 80.44

18.30 0.69 204.43 288.04 370.11 75.89 39.66 97.36 4.25 27.70 12.67 17.59 7.80 24.65 14.05 41.49 29.09 15.04 10.42 42.95 16.68 23.31 50.44 57.03 83.39 39.29 72.06 183.03 59.24 37.37 83.32 2047.34

0.00 0.78 0.24 25.16 164.61 4.02 0.75 16.72 0.00 0.04 0.25 0.10 0.00 0.07 0.43 0.00 0.00 0.23 0.13 12.48 0.11 0.15 0.00 16.81 1.74 0.00 24.96 189.20 35.85 15.56 74.07 502.66

0.01 0.05 3.82 0.00 11.13 0.56 1.45 4.29 1.39 0.00 0.00 20.62 6.36 0.00 1.48 7.69 7.00 13.37 0.97 2.13 0.00 9.26 20.82 0.00 5.58 0.44 0.85 0.00 0.00 0.00 0.00 73.97

Urbanized land

Mixed land

4.13 4.33 12.11 7.20 9.53 8.95 4.78 9.43 2.88 11.88 12.23 16.21 7.66 9.06 21.80 21.35 16.13 13.39 17.15 11.83 1.82 5.66 20.42 2.36 2.29 2.77 8.24 4.75 6.91 4.67 13.45 293.08

16.76 4.92 150.64 267.73 215.28 86.67 79.23 86.57 6.53 31.01 10.44 8.51 3.01 44.34 29.92 53.81 36.53 10.23 34.70 54.88 19.41 20.75 59.58 32.24 91.68 41.17 43.52 0.00 101.51 25.69 150.13 1819.58

respectively. The increased carbon storage in Shandong, Sichuan, Hunan, Anhui and Guangdong provinces as well as the city of Beijing and the Zhejiang Province also obviously will not show much of an increase in carbon storage with amounts all below 10 Tg. The predicted increase in carbon storage is greatly affected by the land area being considered, terrestrial ecosystem carbon densities, as well as the level of economic and social development occurring in different regions. For example, the cities of Beijing and Shanghai have smaller land areas and this will directly affect the ability of these areas to increase carbon storage. For Shandong Province, its lower 800 700 600 500 400 300 200 100 0

Beijing Tianjin Hebei Shanxi InnerM ongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Xizang Shaanxi Gansu Qinghai Ningxia Xinjiang

2020 under the current land use patterns planned by local governments, and will reach 103,334.05 Tg under the land use structure optimized in this paper for 2020; the optimal land use structure discussed here will increase carbon storage by 1671.67 Tg compared with the land use structure in 2005 and 438.53 Tg compared with the land use structure planned by the local government in 2020, with a relative increase of 1.64% and 0.43%, respectively. Because of regional differences, the potential to increase terrestrial ecosystem carbon storage differed widely for different regions in China. Here, we made a comparison between the optimized land use structure in 2020 and land use structure in 2005 based on our modeled amounts and the relative increases (Figs. 3 and 4). As Fig. 3 shows, when compared with the land use structure in 2005, the amount of carbon storage under the optimized land use structure in 2020 will increase for all regions in China, Inner Mongolia will increase the most with 692.51 Tg which makes up 41.47% of the total increased carbon storage in China. The Xinjiang and Gansu regions both will increase obviously with 182 Tg and 132.57 Tg, respectively. Qinghai, Shanxi, Jilin and Shaanxi provinces will increase 51.22–85.74 Tg, but the optimized land use structure will lead to little increase in carbon storage in the Tianjin, Shanghai and Fujian areas, which can only increase 0.89, 1.22 and 1.49 Tg,

Carbon storage(Tg)

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Xizang Shaanxi Gansu Qinghai Ningxia Xinjiang China

Agricultural land

Fig. 3 – Increase of absolute amounts of carbon storage that the optimized land use structure in 2020 higher than land use structure in 2005 for different regions in China.

200 150 100

0

Fig. 5 – Increase of absolute amounts of carbon storage that the optimized land use structure in 2020 higher than the land use structure planned by local governments for different regions in China.

We also made a comparison between the optimized land use structure in 2020 and land use structure in 2020 as planned by local governments to examine its effect on enhancing carbon storage. Fig. 5 shows the absolute amounts carbon storage under the optimized land use structure that higher than under land use structure in 2020 as planned by local governments, while Fig. 6 shows the relative increased percentage of carbon storage under the optimized land use structure when compared with land use structure in 2020 as planned by local governments for different regions in China. As Figs. 5 and 6 show, the optimized land use structure is better at enhancing terrestrial ecosystem carbon storage compared with the planned land use structure designed by local governments for most regions. Inner Mongolia still has the largest increase (214.65 Tg) compared with land use structure in 2005, and the relative increase is also high (1.47%). The potential to increase carbon storage in Xizang, Shanxi, Hebei, Gansu, Shaanxi, Jilin and Heilongjiang are also significant, with increases 38.67–12.62 Tg predicted, but coastal regions such as Zhejiang, Shanghai, Shandong and Guangdong have a little ability to increase their levels of carbon storage. Ningxia has the highest relative increase of 3.17%, and followed by Beijing, Inner Mongolia, Hainan, Hebei, Ningxia and Shanghai with their relative increases all higher than 1%. Regions such as Inner Mongolia, Hebei, Shaanxi and Gansu have a greater opportunity to optimize their levels of carbon storage because they can both increase the actual amount stored and create a relative increase. But areas such as 3.5 3 2.5 2 1.5 1 0.5 0

Beijing Tianjin Hebei Shanxi InnerM ongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Xizang Shaanxi Gansu Qinghai Ningxia Xinjiang

carbon densities on its agricultural land may be able to greatly decrease its carbon storage. But for Sichuan, Guangdong, Zhejiang and Hunan provinces, their land areas are not obviously smaller compared with other regions and the carbon densities of their agricultural land are not lower. But since they need additional economic and social development, future urbanized land will have to occupy large areas of land currently used for agriculture. This will greatly decrease the carbon storage in local vegetation cover and soil. So, after meeting the demand for land needed for economic and social development, the optimization effect will be greatly limited and the effect will not be obvious. Since the amount of carbon storage is determined by various factors, to analyze the effect of optimized land use structure in 2020 more precisely, we calculated the relative increase compared with 2005 levels of carbon storage based on the 2005 land use structure for different regions (Fig. 4). As Fig. 4 shows, Ningxia and Shanxi are projected to increase their levels of carbon storage the most from 2005 to 2020 with relative increases of 7.87% and 6.67%, respectively. The areas of Inner Mongolia, Beijing, Gansu, Hainan, Shaanxi, Jiangsu and Hebei can increase 4.9%, 4.73%, 3.39%, 2.69%, 2.5%, 2.28% and 2.1%, respectively. Sichuan will increase the least (0.05%), and minor increases in carbon storage also will occur in Anhui, Xizang, Guangdong, Zhejiang, Shandong, Hunan, Heilongjiang and Fujian (0.54%, 0.45%, 0.44%, 0.41%, 0.27%, 0.25%, 0.21% and 0.1%, respectively). The high relative increases modeled for Inner Mongolia, Shanxi, Shaanxi and Gansu corresponds well with their historic high increased levels of carbon storage, and the low relative increases in Guangdong, Anhui, Zhejiang, Shandong, Hunan, and Fujian corresponds well with their historic low levels of change. But some regions contradicted this general rule; areas such as Xinjiang and Qinghai which have large increases in the measured amount of carbon storage but have small relative increases, and for Beijing, although it has a small measured increase, it has a high relative increase. So, the effect of increasing carbon storage cannot only be measured by the absolute increase but also needs to be measured as a relative increase. Optimized land use structure with high measured increases and higher relative increases seems more effective; or in other words, areas with a high potential carbon storage are areas where optimization will be easier, such as Inner Mongolia, Gansu, Shaanxi and Shanxi.

50 Beijing Tianjin Hebei Shanxi InnerMongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Xizang Shaanxi Gansu Qinghai Ningxia Xinjiang

Carbon storage(Tg)

Fig. 4 – Relative increase of carbon storage that the optimized land use structure in 2020 higher than land use structure in 2005 for different regions in China.

57

250

Percentage(%)

9 8 7 6 5 4 3 2 1 0

Beijing Tianjin Hebei Shanxi InnerMongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Xizang Shaanxi Gansu Qinghai Ningxia Xinjiang

Percentage(%)

environmental science & policy 25 (2013) 50–61

Fig. 6 – Relative increase of carbon storage that the optimized land use structure in 2020 higher than the land use structure planned by local governments for different regions in China.

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Anhui, Zhejiang, Shandong and Guangdong will have difficulty increasing either the actual or relative amounts of carbon storage. This can also be explained by different locally developing policies as discussed above.

5.

Discussion

In 2008, for the first time China’s carbon emissions from energy consumption exceeded those of the United States. Land use change brought about by human activities is a main driver in changes to terrestrial ecosystem carbon storage (Su et al., 2006; Jiao et al., 2010; Zhong et al., 2006). Lowering carbon emission or increasing carbon storage by adjusting land use structure has been studied in some local areas of China and has been proved to be an effective way to readers carbon emissions (Yu and Wu, 2011; Tang et al., 2009; Zhong et al., 2006), but this has not been carried out at a national scale. The differences of potential to reduce carbon emissions brought by adjusting land use structure among regions have not been studied. Optimizing low-carbon land use structure in different regions of China seems very necessary and meaningful. The aim of this paper is to propose a feasible and effective method for land managers and policy makers to consider ways to reduce carbon emissions. Our study presented that woodland has the highest carbon density in all regions, which can be explained by the fact woodland always have high levels of biomass in vegetation (Fang et al., 2007). Also, woodlands create more residual vegetation which can also enhance levels of carbon storage in the soil (Chuai et al., 2012). Guo and Gifford (2002) indicate, on average, the soil C stock increases by 18% after land-use changes from agricultural crops to forest plantations. In some regions, cultivated land has higher carbon densities than pastureland. This can be explained by the fact carbon densities of cultivated land calculated in this paper are the mean values of cultivated land, garden land and other agricultural land, and garden land always has higher levels of biomass than pastureland (Lai, 2009), which will increase carbon density. In some regions urbanized land has higher carbon densities than mixed land. This can be explained by the vegetation scattered among various types of urbanized land, which is compared with mixed land mainly including open water and unused land and is rarely vegetated. This lack of vegetation will greatly decrease the carbon densities both in vegetation and soil. Also, towns have a variety of carbon sources, including vegetation such as leaves, twigs and weeds, substantial amounts of household garbage, and organic waste produced by urban industries (Zhang and Zhou, 2006; Sun et al., 2009). Soil in urban areas is also generally compacted and sealed which can halt the loss of carbon already present in the soil (Zhao, 2012). By adjusting land use structure we increase carbon storage in China. The essence is to increase the area of land with high carbon density such as woodland and limit or decrease the extent of land with low carbon density such as urbanized land. But with rapid urban development in China, cultivated land is quickly disappearing compared with 2005 for most regions, and cities are coming under significant pressure to protect cultivated land. For garden land, pasture land and other

agricultural land, the land area involved is much smaller and can be adjusted flexibly based on local development plans, but the changes are not expected to be obvious and their contribution to increased carbon storage will not be significant. The model predicts the area covered by woodland will increase the most obviously. Woodlands contributed the most to increased carbon storage, but the increasing trend of urbanization and decreasing trend of other land use types will make it difficult to change these trends since the economy is developing rapidly. The effect varied widely from region to region because of regional differences. Inland regions such as Inner Mongolia, Shanxi, Gansu and Shaanxi always have greater potential to increase carbon storage, but the potential in coastal regions such as Guangdong, Zhejiang, Shandong and Fujian is limited because of rapid social and economic development. The optimized land use structure in 2020 can bring about a 1.64% and 0.43% relative increase of carbon storage compared with land use structure in 2005 and in 2020 as planned by local government, respectively, so, is the increase significant? Since China covers a large area of the world, the relatively small increases of 1.64% and 0.43% will result in increasing carbon storage by 1671.67 Tg and 438.52 Tg, respectively. This accounts for a 185.78% and 48.89% increase in carbon absorbed by global terrestrial ecosystems of 900 Tg/a (Lal, 1999). The absolute increase amount of carbon storage between optimized land use structure in 2020 and land use structure in 2005 can offset most of carbon emissions brought on by increased energy consumption in recent years. Many scholars calculated the amount of carbon emission from energy consumption as 1.56 Pg (Lai, 2009) and in 2005, 1.66 Pg (Xu, 2010) in 2006, 1.78 Pg (CDIAC, 2011) and 1.46 Pg (Zhao et al., 2011) in 2007. So China’s ability to increase carbon sequestration has the potential to make a significant contribution to reducing global atmospheric carbon levels. Also, many land-use types currently undergoing natural succession can continue to accumulate carbon for a very long time (Cantarello et al., 2010; Luyssaert et al., 2008), and since our analysis was based on the assumption that none of the land-use types are either gaining or losing carbon over time, in reality, the increase amounts in 2020 may much higher than the value we calculated here. There are also other ways to lower atmospheric CO2 concentrations, such as land management, tillage practices and so on. Lee et al. (2009), Cao (2008) and Mandal et al. (2007) reported a combined use of inorganic fertilizer and organic manure was the best way to increase SOC even in deep soil (Hu et al., 2010). Zhang et al. (2001) indicated crop rotation systems which include corn and soybeans usually have higher SOC content than rice-wheat rotation systems in the North Huai region of China. If implemented correctly, other methods will also be useful in reducing carbon emission, but these measurements are usually carried out in certain ecosystems or local area and cannot be applied to large scales such as the provincial and national level. Land use types cannot always be changed, so we can say our study was carried out at a macroscopic scale and measures of land management and tillage practices occur at a microscopic scale, and the effect of increasing carbon storage in our study is more obvious. Limiting fuel combustion is another direct and effective way to reduce carbon emissions (Li et al., 2010; Sun et al., 2010). Many

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activities of energy consumption are tied to certain land uses; for example, industrial activities are usually carried out on urbanized land and agricultural activities usually occur on cultivated lands. Lands with intensive human activities, such as urbanized land, usually have high carbon emissions from energy consumption. Adjusting land use structure can obviously affect human activities and carbon emissions. For example, the conversion of urbanized land to other land types will reduce industrial activities and reduce energy consumption (Lai, 2009; Zhao et al., 2011; Li et al., 2008; Liu et al., 2010a,b), so our optimized land use plan cannot only increase carbon storage in the terrestrial ecosystem but can also reduce carbon emissions from energy consumption indirectly. Also, carbon rich land use types can also provide benefits other than carbon storage, such as water conservation and flood control, erosion control, fuel, food, biodiversity and so on (Costanza et al., 1997; Yue et al., 2007; Konarska et al., 2002). In summary, despite the simplifying assumptions we made, the optimized land use structure could be of value to land managers and policy makers, for it cannot only meet land use demands for economic and social development, but also can increase carbon storage amounts which can help to reduce the emission reduction pressure China faces. Also, carbon rich land use types can also provide other benefits as discussed above. So this method is worth serious consideration by policy makers.

Acknowledgments Financial assistance for this work was provided by the National Social Science Foundation of China, No. 10ZD&M030; the Non-profit Industry Financial Program of Ministry of Land and Resources of China, No. 200811033; A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and The National Natural Science Foundation of China (No. 40801063 and No. 40971104).

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Xiaowei Chuai, Ph.D Candidate, specialized in Land use and resources.

Wanjing Wang, Ph.D Candidate, specialized in environmental planning and management.

Xianjin Huang, Professor, specialized in land use and resources & environmental economics.

Jiawen Peng, Master, specialized in land use and land planning.

Li Lai, Ph.D, specialized in land use and resources.

Rongqin Zhao, Ph.D, lecturer, specialized in carbon cycle and lowcarbon economy.