3.1.1. Identifying the Drivers of Land-Cover Change in Serbia during the Three Sub-Periods
The mentioned classification by [
8] as the first element of the framework was slightly modified based on the academic literature and depending on the availability of statistical data and specific conditions in Serbia. More specifically, the set of the distinctive variables of the population were separated from Cultural drivers into a distinct group—Demographic drivers. The data on the prices of agricultural and forest products from the original classification were abstracted within the general assessment of the market and retail growth. “Topography and spatial configuration” were replaced with “relief; current land cover, infrastructure and public services”, because we expected it to be readily understood by respondents from Serbia. E-government was added to Technological drivers due to its rising importance regarding land-use management not only in Serbia.
As a result, we have established the following classification of drivers:
Political–institutional drivers include sectoral policies and strategies relevant to the process and legal documents relevant to the process including property rights, spatial and urban policies and plans;
Economic drivers include structural economic change, especially in activities relevant to the process, taxes and subsidies, market and retail growth (real-estate, goods and services market);
Natural–spatial drivers include relief, climate, hydrology, soil characteristics and natural hazards, current land cover, infrastructure and public services;
Cultural drivers include public attitudes, values and beliefs, individual and household lifestyles and behaviour;
Demographic drivers include population size and density, age and education structures and migrations;
Technological drivers include modernization of society, of the land management system and of local community units (e-government).
A literature review, national statistical data and our previous research [
31] made it possible to specify the drivers’ main features for Serbia in the three sub-periods.
The 1990–2000 Sub-Period
Political–institutional: sectoral policies and laws intended to delay the transition in the context of Yugoslavia’s disintegration, wars, centralization and international isolation.
Economic: the international economic sanctions hindered access to markets; sales dropped; production and the growth of services collapsed; grey and black economy grew to an extreme extent, subsidies in all areas were reduced.
Natural–spatial: reduced production and exploitation of resources led to a relative improvement of soil and hydrological features; neglected and underdeveloped infrastructure and public services. The percentage of the first-level CLC classes in total area from 1990 to 2000: artificial surfaces from 3.18% to 3.31%; agricultural areas from 57.09% to 57.19%; forest and seminatural areas from 38.37% to 38.08%; wetlands from 0.27% to 0.30%; water bodies from 1.09% to 1.12%.
Demographic: population of 7,822,795/population density 88.53 people per km2; average age 37.1; 40% with completed secondary education, 5.06% with higher education. Intensive emigration to developed countries and immigration of Serbian and other refugees from war-affected areas of ex-Yugoslavia.
Cultural: relative domination of non-civic attitudes, values and beliefs regarding ethnic tolerance, inter-confessional harmony, human equality, tolerance of sexual minorities and the rule of law. Traditional, agricultural and patriarchal lifestyles in rural communities in contrast to the lifestyles in urban communities. Behaviours focused on self-actualization and health, environmental, social and economic rights and responsibilities were largely marginalized. Consumerism was limited due to the poor offer of products and services and low income.
Technological: stagnating technological modernization and the emerging uptake of information technology (IT) among individual users and institutions; delays in maintaining and improving the land-management system; e-government did not exist.
The 2000–2006 Sub-Period
Political–institutional: abrupt transition towards capitalism, democratization and decentralization supported by the international and EU community. Intensified development of strategies, plans and laws aimed at the accession to the EU and fostering reforms with a special focus on spatial interventions and regulation.
Economic: access to new markets and sales growth due to the lifting of the economic sanctions; intensification and relative diversification of space-consuming, low-productivity and income-creating activities. Controversial privatization of state-owned factories and further growth of the services sector; initial steps in establishing systems for tax collection and subsidy assignment, especially for new jobs.
Natural–spatial: intensified degradation processes and the pollution of natural resources due to increased production; the reconstruction and moderate development of infrastructure and public services. Intensified pressure on agriculture and forest land use, especially in the close vicinity of urban areas. Percentage of the first-level CLC classes in the total area from 2000–2006: artificial surfaces from 3.31% to 3.63%; agricultural areas from 57.19% to 55.63%; forest and seminatural areas from 38.08% to 39.25%; wetlands from 0.30% to 0.33%; water bodies from 1.12% to 1.16%.
Demographic: population of 7,498,001/population density 84.85 people per km2; average age 40.2; 41.19% with completed secondary education, 6.62% with higher education. Significantly decreased intensity of migrations, except for internal migrations from rural to urban settlements and from small urban settlements to major urban centres.
Cultural: democratic change in 2000, accompanied with lingering crisis and corruption; four distinct social groups subscribed to different ratios of civic and non-civic attitudes, values and beliefs: hard liberals, soft liberals, ultra-nationalists and a growing apolitical group. The differences between the rich minority and the poor majority were more obvious. The same applies to the differences between rural, traditional and patriarchal and relatively modernized municipalities. Behaviours related to the mentioned rights and responsibilities were promoted. Consumerism was invigorated due to a better offer of products and services, higher incomes and availability of loans.
Technological: sporadic and moderate technological modernization in the economy. Increasingly massive implementation of IT in governance; land-management system was relatively improved due to various international projects and donations.
The 2006–2012 Sub-Period
Political–institutional: intensified development of strategies, plans and laws aimed at the accession to the EU and national spatial interventions, and diminished effects of regulation, democratization and decentralization. The implementation of austerity measures in the public sector after the 2008 Global Economic Crisis.
Economic: increased production and the further growth of the services sector; intensified market expansion and sales growth; stricter tax collection and the diversification of subsidy opportunities, accompanied with significantly reduced funding allocated for subsidies after the 2008 Global Economic Crisis.
Natural–spatial: intensified degradation processes and pollution, accompanied with the negative impact of global climate changes and increased natural hazard risk, especially flood risk; focus was on infrastructure development, with moderate results, while the public service development was affected by austerity measures. Intensified problems related to over- or under-used and polluted land. Percentage of the first-level CLC classes in the total area from 2006–2012: artificial surfaces from 3.63% to 3.67%; agricultural areas from 55.63% to 55.68%; forest and seminatural areas from 39.25% to 39.27%; wetlands from 0.33% to 0.33%; the share of water bodies has not changed (1.16%).
Demographic: population of 7,186,862/population density 81.33 people per km2; average age 42.2; 48.93% with completed secondary education, 10.59% with higher education. Low birth rates and re-intensified emigration, especially brain drain towards major urban areas in the country and to developed countries.
Cultural: lingering and poor transition resulted in the growing apolitical group, while hard liberals and ultra-nationalists experienced further polarization. The urban–rural and rich–poor polarization in terms of lifestyle was intensified. Further promotion of tolerance and rights and responsibilities was undermined by the ‘delegitimization’ of the EU integration. Relatively increased consumerism.
Technological: modernization in industry and further growth and improvement of IT application were increasingly dependent on the recognition of benefits and the availability of economic resources; establishment of a hybrid land-management system in terms of technological improvements (in some areas, the system was highly developed, whereas, in others, it was neglected and outdated); improving infrastructure towards developing the e-government.
3.1.2. Identified LULCCs in Serbia for Three Sub-Periods
The land-cover analysis and the identification of LULCCs in Serbia between 1990 and 2012 were based on the CORINE Land Cover (CLC) for 1990, 2000, 2006 and 2012 and CORINE Land Cover Change (CLCC) databases for Serbia for the three sub-periods (1990–2000, 2000–2006 and 2006–2012). They were processed using the ArcGIS Desktop 10.5 software package. In the territory of Serbia, 29 CLC land-cover classes were identified for the 1990–2012 period.
After the CLC database had been created, the Conversion Table devised by [
11] was applied. Six major LULCCs were identified in Serbia in three sub-periods. The category “other changes” was not taken into consideration in the land-cover flow context, similar to the “no change” category, which entails the changes observed among the third level of CLC classes within each second-level CLC class [
11].
The numeric values and share of six types of LULCC in total LULCCs in Serbia in the three sub-periods is given in
Table 1.
3.1.3. Assessing the Importance of Drivers for Each LULCC
The assessment of the drivers’ importance in each LULCC in Serbia in the 1990–2012 period was performed using the expert survey method.
In order to ensure the satisfactory quality of the expert survey in this study, the following procedure was applied: the survey was based on a list of six major groups of drivers associated with the six processes of LULCC in Serbia; the importance of the drivers was assessed for the three sub-periods. The rating was based on a scale ranging from 0 (no influence) to 10 (the greatest impact). Due to the complexity of the topic and the desire to decrease the random error in the survey, the option “I cannot say” was added to the answer choices, as well as the possibility to make comments [
37] for the cases where experts perceived some incoherence with the task they should perform.
The “crossed design” of the survey was chosen in order to reflect the drivers-LULCCs matrix. Subsequently, we identified the target units which included the drivers, the sub-periods and the six types of LULCCs previously identified in Serbia. Since the aggregate error is reduced when a wider range of individuals with different perspectives and knowledge on the target unit contribute to the aggregate [
14], we considered a specific “pool of experts”. A more detailed description of the process of selecting and communicating with experts is given in the
Supplementary Materials.
In total, 17 experts were contacted and 13 assessments were received, while two were excluded due to the insufficient information provided. Out of 11 assessments that were analysed, 8 were from academics (three spatial planners, one geographer, two forest engineers, one economist and one land surveyor all with highest competence in land change issues) and 3 from the invited professionals. The mean (M) age of our respondents was 42.08 (ages ranged from 35 to 54), and Sd (standard deviation) was 6.65. More information about how the survey was conducted can be found in
Supplementary Materials.
Finally, descriptive statistics was used to determine the mean values, i.e., the importance of the various drivers of LULCCs in Serbia in the three sub-periods. The process is explained in the following sections.