Drivers Of Land Use Change Scenarios

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Predicting the impact of land-use change on biodiversity 01 September 2014 Share. Demand for more agricultural land is one of the main drivers of habitat loss and degradation. Biodiversity change under different scenarios of land-use change was predicted for nearly 4,000 topical and sub-tropical forest habitat dwelling taxa including. The past drivers of land use. Synthesis of the ALARM land use change scenarios for Europe. The effect of land use change (LUC) on future SOC stocks was estimated using the. Unsustainable changes in land use are recognised as main drivers of environmental change which result in severe degradation and/or losses of ecosystem services at the global scale. A large body of research has demonstrated that land use and land cover changes play a major role in determining the Earth’s climate system. Projections on global land use and respective scenarios for 2030 and 2050. It is based on an overview of studies and models. The paper takes into account the vari-ation of relevant parameters between scenarios to derive a synopsis of key “drivers“ of future global land use which will be used in later project phases. Using emissions scenarios as land use change protocolA key objective of the New York Climate & Health Project was the definition of several scenarios of future climate and land cover conditions. In response to the project demand for several land cover conditions, a set of land use change scenarios were created.

Climate changescenarios orSocioeconomic scenarios are projections of future greenhouse gas (GHG) emissions used by analysts to assess future vulnerability to climate change.[1] Producing scenarios requires estimates of future population levels, economic activity, the structure of governance, social values, and patterns of technological change. Economic and energy modelling (such as the World3 or the POLES models) can be used to analyse and quantify the effects of such drivers.

  • 1Emissions scenarios
  • 2Quantitative emissions projections

Emissions scenarios[edit]

Global futures scenarios[edit]

These scenarios can be thought of as stories of possible futures. They allow the description of factors that are difficult to quantify, such as governance, social structures, and institutions. Morita et al. assessed the literature on global futures scenarios.[2] They found considerable variety among scenarios, ranging from variants of sustainable development, to the collapse of social, economic, and environmental systems. In the majority of studies, the following relationships were found:

  • Rising GHGs: This was associated with scenarios having a growing, post-industrial economy with globalization, mostly with low government intervention and generally high levels of competition. Income equality declined within nations, but there was no clear pattern in social equity or international income equality.
  • Falling GHGs: In some of these scenarios, GDP rose. Other scenarios showed economic activity limited at an ecologically sustainable level. Scenarios with falling emissions had a high level of government intervention in the economy. The majority of scenarios showed increased social equity and income equality within and among nations.

Morita et al. (2001) noted that these relationships were not proof of causation.

No strong patterns were found in the relationship between economic activity and GHG emissions. Economic growth was found to be compatible with increasing or decreasing GHG emissions. In the latter case, emissions growth is mediated by increased energy efficiency, shifts to non-fossil energy sources, and/or shifts to a post-industrial (service-based) economy.

Factors affecting emissions growth[edit]

Development Trends[edit]

In producing scenarios, an important consideration is how social and economic development will progress in developing countries.[3] If, for example, developing countries were to follow a development pathway similar to the current industrialized countries, it could lead to a very large increase in emissions. Emissions do not only depend on the growth rate of the economy. Other factors include the structural changes in the production system, technological patterns in sectors such as energy, geographical distribution of human settlements and urban structures (this affects, for example, transportation requirements), consumption patterns (e.g., housing patterns, leisure activities, etc.), and trade patterns the degree of protectionism and the creation of regional trading blocks can affect availability to technology.

Baseline scenarios[edit]

A baseline scenario is used as a reference for comparison against an alternative scenario, e.g., a mitigation scenario.[4] In assessing baseline scenarios literature, Fisher et al., it was found that baseline CO2 emission projections covered a large range. In the United States, electric power plants emit about 2.4 billion tons of carbon dioxide (CO2) each year, or roughly 40 percent of the nation's total emissions. The EPA has taken important first steps by setting standards that will cut the carbon pollution from automobiles and trucks nearly in half by 2025 and by proposing standards to limit the carbon pollution from new power plants.[5]

Factors affecting these emission projections are:

  • Population projections: All other factors being equal, lower population projections result in lower emissions projections.
  • Economic development: Economic activity is a dominant driver of energy demand and thus of GHG emissions.
  • Energy use: Future changes in energy systems are a fundamental determinant of future GHG emissions.
    • Energy intensity: This is the total primary energy supply (TPES) per unit of GDP.[6] In all of the baseline scenarios assessments, energy intensity was projected to improve significantly over the 21st century. The uncertainty range in projected energy intensity was large (Fisher et al. 2007) .
    • Carbon intensity: This is the CO2 emissions per unit of TPES. Compared with other scenarios, Fisher et al. (2007) found that the carbon intensity was more constant in scenarios where no climate policy had been assumed. The uncertainty range in projected carbon intensity was large. At the high end of the range, some scenarios contained the projection that energy technologies without CO2 emissions would become competitive without climate policy. These projections were based on the assumption of increasing fossil fuel prices and rapid technological progress in carbon-free technologies. Scenarios with a low improvement in carbon intensity coincided with scenarios that had a large fossil fuel base, less resistance to coal consumption, or lower technology development rates for fossil-free technologies.
  • Land-use change: Land-use change plays an important role in climate change, impacting on emissions, sequestration and albedo. One of the dominant drivers in land-use change is food demand. Population and economic growth are the most significant drivers of food demand.[7][dubious]

Quantitative emissions projections[edit]

A wide range of quantitative projections of greenhouse gas emissions have been produced.[8] The 'SRES' scenarios are 'baseline' emissions scenarios (i.e., they assume that no future efforts are made to limit emissions),[9] and have been frequently used in the scientific literature (see Special Report on Emissions Scenarios for details).[10]Greenhouse gas#Projections summarizes projections out to 2030, as assessed by Rogner et al.[11] Other studies are presented here.

Individual studies[edit]

In the reference scenario of World Energy Outlook 2004,[12] the International Energy Agency projected future energy-related CO2 emissions. Emissions were projected to increase by 62% between the years 2002 and 2030. This lies between the SRES A1 and B2 scenario estimates of +101% and +55%, respectively.[13] As part of the IPCC Fourth Assessment Report, Sims et al. (2007) compared several baseline and mitigation scenarios out to the year 2030.[14] The baseline scenarios included the reference scenario of IEA's World Energy Outlook 2006 (WEO 2006), SRES A1, SRES B2, and the ABARE reference scenario. Mitigation scenarios included the WEO 2006 Alternative policy, ABARE Global Technology and ABARE Global Technology + CCS. Projected total energy-related emissions in 2030 (measured in GtCO2-eq) were 40.4 for the IEA WEO 2006 reference scenario, 58.3 for the ABARE reference scenario, 52.6 for the SRES A1 scenario, and 37.5 for the SRES B2 scenario. Emissions for the mitigation scenarios were 34.1 for the IEA WEO 2006 Alternative Policy scenario, 51.7 for the ABARE Global Technology scenario, and 49.5 for the ABARE Global Technology + CCS scenario.

Drivers Of Land Use Land Cover Change

Garnaut et al. (2008)[15] made a projection of fossil-fuel CO2 emissions for the time period 2005-2030. Their “business-as usual” annual projected growth rate was 3.1% for this period. This compares to 2.5% for the fossil-fuel intensive SRES A1FI emissions scenario, 2.0% for the SRES median scenario (defined by Garnaut et al. (2008) as the median for each variable and each decade of the four SRES marker scenarios), and 1.6% for the SRES B1 scenario. Garnaut et al. (2008) also referred to projections over the same time period of the: US Climate Change Science Program (2.7% max, and 2.0% mean), International Monetary Fund's 2007 World Economic Outlook (2.5%), Energy Modelling Forum (2.4% max, 1.7% mean), US Energy Information Administration (2.2% high, 1.8% medium, and 1.4% low), IEA's World Energy Outlook 2007 (2.1% high, 1.8 base case), and the base case from the Nordhaus model (1.3%).

The central scenario of the International Energy Agency publication World Energy Outlook 2011 projects a continued increase in global energy-related CO
2 emissions, with emissions reaching 36.4 Gt in the year 2035.[16] This is a 20% increase in emissions relative to the 2010 level.[16]

UNEP 2011 synthesis report[edit]

The United Nations Environment Programme (UNEP, 2011)[17]:7 looked at how world emissions might develop out to the year 2020 depending on different policy decisions. To produce their report, UNEP (2011)[17]:8 convened 55 scientists and experts from 28 scientific groups across 15 countries.

Projections, assuming no new efforts to reduce emissions or based on the 'business-as-usual' hypothetical trend,[18] suggested global emissions in 2020 of 56 gigatonnesCO
2-equivalent (GtCO
2-eq), with a range of 55-59 GtCO
2-eq.[17]:12 In adopting a different baseline where the pledges to the Copenhagen Accord were met in their most ambitious form, the projected global emission by 2020 will still reach the 50 gigatonnes CO
2.[19] Continuing with the current trend, particularly in the case of low-ambition form, there is an expectation of 3° Celsius temperature increase by the end of the century, which is estimated to bring severe environmental, economic, and social consequences.[20] For instance, warmer air temperature and the resulting evapotranspiration can lead to larger thunderstorms and greater risk from flash flooding.[21]

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Other projections considered the effect on emissions of policies put forward by UNFCCC Parties to address climate change. Assuming more stringent efforts to limit emissions lead to projected global emissions in 2020 of between 49-52 GtCO
2-eq, with a median estimate of 51 GtCO
2-eq.[17]:12 Assuming less stringent efforts to limit emissions lead to projected global emissions in 2020 of between 53-57 GtCO
2-eq, with a median estimate of 55 GtCO
2-eq.[17]:12

Notes[edit]

  1. ^Carter, T.R.; et al. (2001). 'Developing and Applying Scenarios. In: Climate Change 2001: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change [J.J. McCarthy et al. Eds.]'. Cambridge University Press, Cambridge, U.K., and New York, N.Y., U.S.A. Retrieved 2010-01-10.
  2. ^Morita, T.; et al. (2001). 'Greenhouse Gas Emission Mitigation Scenarios and Implications. In: Climate Change 2001: Mitigation. Contribution of Working Group III to the Third Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz et al. Eds.]'. Cambridge University Press, Cambridge, U.K., and New York, N.Y., U.S.A. Retrieved 2010-01-10.
  3. ^Fisher, B.S.; et al. (2007). 'Issues related to mitigation in the long term context. In: Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz et al. Eds.]'. Cambridge University Press, Cambridge, U.K., and New York, N.Y., U.S.A. Retrieved 2009-05-20.
  4. ^IPCC (2007c). 'Annex. In: Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz et al. Eds.]'. Cambridge University Press, Cambridge, U.K., and New York, N.Y., U.S.A. Retrieved 2009-05-20.
  5. ^'Using the Clean Air Act to Sharply Reduce Carbon Pollution from Existing Power Plants'. Natural Resources Defense Counsel. Retrieved October 9, 2013.
  6. ^Rogner, H.-H.; et al. (2007). 'Introduction. In: Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz et al. Eds.]'. Cambridge University Press, Cambridge, U.K., and New York, N.Y., U.S.A. Retrieved 2009-05-20.
  7. ^Fisher, B.S.; et al. (2007). ''3.2.1.6 Land-use change and land-use management.' In [book chapter]: 'Issues related to mitigation in the long term context.' In [book]: 'Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz et al. Eds.]'. Print version: Cambridge University Press, Cambridge, U.K., and New York, N.Y., U.S.A. This version: IPCC website. Retrieved 2010-03-18.
  8. ^Fisher; et al., 'Chapter 3: Issues related to mitigation in the long-term context', Sec. 3.1 Emissions scenariosMissing or empty title= (help), in IPCC AR4 WG3 2007
  9. ^Morita; et al., 'Chapter 2, Greenhouse Gas Emission Mitigation Scenarios and Implications', Sec. 2.5.1.1 IPCC Emissions Scenarios and the SRES ProcessMissing or empty title= (help), in IPCC TAR WG3 2001.
  10. ^Karl, TR; et al., eds. (2009), 'Global climate change', Global Climate Change Impacts in the United States, New York, NY, USA: Cambridge University Press, p. 22, ISBN978-0-521-14407-0, archived from the original on 2012-09-15
  11. ^Rogner, H.-H.; et al., 'Introduction', Sec 1.3.2 Future outlookMissing or empty title= (help), in IPCC AR4 WG3 2007
  12. ^IEA (2004). World Energy Outlook 2004(PDF). World Energy Outlook website. p. 74.
  13. ^Section 4.3.1, Fossil fuels, in IPCC AR4 WG3 2007.
  14. ^Section 4.4.1, Carbon dioxide emissions from energy supply by 2030, in IPCC AR4 WG3 2007.
  15. ^Garnaut, R.; Howes, S.; Jotzo, F.; Sheehan, P. (2008). 'Emissions in the Platinum Age: the implications of rapid development for climate-change mitigation'(PDF). Oxford Review of Economic Policy. 24 (2): 392. doi:10.1093/oxrep/grn021. Archived from the original(PDF) on 2012-03-21. Retrieved 2012-09-08.
  16. ^ abInternational Energy Agency (IEA) (2011), World Energy Outlook 2011: Fact Sheets(PDF), Paris, France: IEA, p. 2, ISBN978-92-64-12413-4
  17. ^ abcdeUNEP (November 2011), Bridging the Emissions Gap: A UNEP Synthesis Report(PDF), Nairobi, Kenya: United Nations Environment Programme (UNEP), ISBN978-92-807-3229-0 UNEP Stock Number: DEW/1470/NA
  18. ^Fozzard, Adrian (2014). Climate Change Public Expenditure and Institutional Review Sourcebook (CCPEIR). Washington, D.C.: World Bank Publications. p. 92.
  19. ^Alam, Shawkat; Bhuiyan, Jahid; Chowdhury, Tareq; Techera, Erika (2013). Routledge Handbook of International Environmental Law. London: Routledge. p. 373. ISBN9780415687171.
  20. ^Govaere, Inge; Poli, Sara (2014). EU Management of Global Emergencies: Legal Framework for Combating Threats and Crises. Leiden: BRILL Nijhoff. p. 313. ISBN9789004268326.
  21. ^van Drunen, M.A.; Lasage, R.; Dorland, C. (2006). Climate Change in Developing Countries: Results from the Netherlands Climate Change Studies Assistance Programme. Cambridge, MA: CAB International. p. 52. ISBN9781845930776.

References[edit]

Land Use Change Analysis

  • IPCC TAR WG3 (2001), Metz, B.; Davidson, O.; Swart, R.; Pan, J. (eds.), Climate Change 2001: Mitigation, Contribution of Working Group III to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, ISBN0-521-80769-7, archived from the original on 2017-02-27 (pb: 0-521-01502-2).
  • IPCC AR4 WG3 (2007), Metz, B.; Davidson, O.R.; Bosch, P.R.; Dave, R.; Meyer, L.A. (eds.), Climate Change 2007: Mitigation of Climate Change, Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, ISBN978-0-521-88011-4 (pb: 978-0-521-70598-1).
Retrieved from 'https://en.wikipedia.org/w/index.php?title=Climate_change_scenario&oldid=904858135'
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Physical and Socioeconomic Driving Forces of Land-Use and Land-Cover Changes: A Case Study of Wuhan City, China

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1School of Environment, Resources and International Trade, Hubei University of Economics, Wuhan 430205, China
2Center of Hubei Cooperative Innovation for Emissions Trading System, Wuhan 430205, China
3School of Public Administration, China University of Geosciences, Wuhan 430074, China
4School of Economics and Management, China University of Geosciences, Wuhan 430074, China

Received 3 December 2015; Revised 17 February 2016; Accepted 14 March 2016

Academic Editor: Elmetwally Elabbasy

Copyright © 2016 Xiangmei Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To investigate precise nexus between land-use and land-cover changes (LUCC) and driving factors for rational urban management, we used remotely sensed images to map land use and land cover (LULC) from 1990 to 2010 for four time periods using Wuhan city, China, as a case study. Partial least squares (PLS) method was applied to analyze the relationships between LUCC and the driving factors, mainly focusing on three types of LULC, that is, arable land, built-up area, and water area. The results were as follows: during the past two decades, the land-use pattern in Wuhan city showed dramatic change. Arable land is made up of the largest part of the total area. The increased built-up land came mainly from the conversion of arable land for the purpose of economic development. Based on the Variable Importance in Projection (VIP), the joint effects of socioeconomic and physical factors on LUCC were dominant, though annual temperature, especially annual precipitation, proved to be less significant to LUCC. Population, tertiary industry proportion, and gross output value of agriculture were the most significant factors for three major types of LULC. This study could help us better understand the driving mechanism of urban LUCC and important implications for urban management.

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1. Introduction

Land-use and land-cover changes (LUCC) increasingly have been regarded as a primary source of global environmental change such as emission of greenhouse gases, global climate change, loss of biodiversity, and loss of soil resources [1–3]. However, the causes of LUCC are complex and change over time and from region to region [4]. In the early 1990s, keeping in view the diverse reasons and causes of land-use change emphasizes the importance of interdisciplinary research to address the issue of land-use change with a particular focus on the human dimensions [5]. LUCC have been led by a set of socioeconomic driving forces and conditioned by different natural endowments [6] that determine the trajectories of landscape development [7]. Understanding the driving mechanisms of LUCC caused by a variety of driving forces is one of the major goals of global change research in recent decades [8–10].

To understand the human and biophysical processes of LUCC, many researchers focus on the various forces driving LUCC, including socioeconomic [11], demographic [12], political [13], technological [14], biophysical [15], and industrial structure [16], to provide effective support for developing urban land planning and management regulations. To comprehensively analyze the driving factor’s effects and mitigate the negative impacts of land-use change, Shu et al. (2014) [17] investigated the effects of various factors, including natural ecoenvironment factors, land control policies, accessibility factors, and neighborhood factors, on urban land expansion during various periods in different regions. Chen et al. (2014) [18] selected industrial structure, GDP, transportation, and policy as the driving factors to study the impacts on urban land expansion and sustainable urban development in Shenzhen and Dongguan. In addition, an integration of biophysical and human factors was applied in the explanation of LUCC dynamics of Mediterranean Europe due to its particular climatic and physical conditions [15]. Obviously, the outstanding characteristics of the studied cases, such as economy, culture, climate, and policy, often were considered as the important driving forces in the explanation of LUCC dynamics. However, though many researches on land-use change have been conducted, climate factors were seldom available on driving analysis of LUCC.

Various research methods were employed to explore the nexus between LUCC and their driving forces. Multivariate regression was used to model how the major forces drive the physical expansion of urban land cover at the global level [19]. Li et al. (2013) [20] applied binary logistic regression to investigate the effects of the selected driving variables on the probability of urban expansion. Analytic hierarchy process (AHP), as a subjective method, provided rigorous quantitative measures to understand the interactive process of urban growth and factors [21]. System dynamics (SD) was combined with CLUE-S model to reflect the complexity of the land-use system [22], regarded as a macrolevel, “top-down” implementation process. However, few attempts have been made to investigate precise nexus between LUCC and driving factors. Partial least squares (PLS) method, as a major regression technique for multivariate data, may handle highly correlated noise-corrupted data sets by explicitly assuming the dependency between variables and estimating the underlying structures [23]. It could effectively reflect the significant PLS components using the cross validation technique [24]. In the paper, PLS was used to accurately reflect the nexus between LUCC and driving factors, meanwhile determining the significant components of the selected driving factors.

In the paper, our main objectives were to address the processes of land-use dynamics in Wuhan city and its integrated driving forces through combining satellite-based efforts at mapping land use and land cover (LULC) and physical, socioeconomic data. Based on RS images in 1990, 1995, 2000, 2005, and 2010, we analyzed the variant change of each LULC type during 4 periods (1990–1995, 1995–2000, 2000–2005, and 2005–2010). Thirteen variables of physical and socioeconomic factors were selected as the potential driving factors of LUCC. Partial least squares (PLS) method is applied to select the important driving factors and analyze the relationships between LUCC and the factors triggering each land-use change type, mainly focusing on three major types of LUCC, that is, arable land, built-up land, and water area. Finally, we suggested some possible management measures that are crucial for future sustainable utilization and management of its existing land resources, for example, managing urban growth and protecting cultivated land.

2. Study Area

Wuhan city, as a central hinterland megalopolis of China, is situated in the east of Jianghan plain and covers over 8494.41 km2 (113°41′–115°05′ E, 29°58′–31°22′ N). Terrain is dominated by flat areas with a surface elevation ranging from 0 to 100 m, and the slope is less than 10°, making up 95.78% of the total. The low hills, which have an elevation between 200 and 400 m and a slope varying from 10 to 25°, constitute 3.89% of its total area, while the mountainous area (elevation 400~800 m; slope >25°) accounts for only 0.33% (Figure 1). The Yangtze River and its largest tributary Han River meet here, which divides Wuhan into three parts, Hankou, Hanyang, and Wuchang, commonly known as “the three towns of Wuhan.’’ The area has the subtropical humid monsoon climate. Its climate feature is obvious, characterized by abundant rainfall, summer heat, and winter cold, with an annual temperature of approximately 15.8–17.5°C and mean annual precipitation of 1150–1450 mm. A huge water network was formed with the Yangtze River as the backbone and supplemented by quantities of lakes or ponds. Therefore, Wuhan city has a reputation of “city of hundred lakes.’’ The water area accounts for 25.6% of the city area.