Urban expansion in 30 megacities of China: categorizing the driving force profiles to inform the urbanization policy

Currently, land use policy makers in developing countries face the challenge to control urban expansion in a rational and sustainable manner. In this context, understanding the urban expansion process and its determinants is essential for designing land use planning and governance strategies. This paper presents a methodological framework based on data mining techniques for exploring the determinants of urban expansion. We employ the Random Forest regression to apportion the relative importance for 33 determinants of urban expansion across 30 megacities in China. Analysis is facilitated by a dataset of urban land information in period 1993–2012 (derived from time-series nighttime light imageries) as the independent variable, and an equivalent dataset of 33 determinants of four categories (economic, demographic, social and natural) as exploratory variables. Results show that the relative importance of determinants varies with cities but shows some similarities. Greatest contribution is observed for the economic determinants, followed by social determinants. Demographic and natural determinants have comparative relative importance. In general, economic, social, and demographic determinants have increasing contribution to urban expansion, but the natural determinants show declining influence on urban expansion with time. Four clusters are identified by a Self-Organizing Map among the 30 megacities, with respect to the driving forces profiles. Urban expansion in these four clusters is respectively dominated by demographic, social, natural, and economic determinants. Based on these profiles, the land use planners can revised the original ‘top-down’ land-driven development model and formulate more locality-oriented measures to control urban expansion in an orderly manner.     

Location and uptake: integrated household and GIS analysis of technology adoption and land use, with application to smallholder dairy farms in Kenya

CIS‐derived measures of location and space have increasingly been used in models of land use and ecology. However, they have made few inroads into the literature on technology adoption in developing countries, which continues to rely mainly on survey‐derived information. Location, with all its dimensions of market access, demographics and agro‐climate, nevertheless remains key to understanding potential for technology use. The measures of location typically used in the adoption literature, such as locational dummy variables that proxy a range of locational factors, now appear relatively crude given the increased availability of more explicit GIs‐derived measures. This paper attempts to demonstrate the usefulness of integrating CIS‐measures into analysis of technology uptake, for better differentiating and understanding locational effects. A set of GIs‐derived measures of market access and agro‐climate are included in a standard household model of technology uptake, applied to smallholder dairy farms in Kenya, using a sample of 3330 geo‐referenced farm households. The three technologies examined are keeping of dairy cattle, planting of specialised fodder, and use of concentrate feed. Logit estimations are conducted that significantly differentiate effects of individual household characteristics from those related to location. The predicted values of the locational variables are then used to make spatial predictions of technology potential. Comparisons are made with estimations based only on survey data, which demonstrate that while overall explanatory power may not improve with CIS‐derived variables, the latter yield more practical interpretations, which is further demonstrated through predictions of technology uptake change with a shift in infrastructure policy. Although requiring large geo‐referenced data sets and high resolution GIS layers, the methodology demonstrates the potential to better unravel the multiple effects of location on farmer decisions on technology and land use.     

Spatially explicit micro-level modelling of land use change at the rural–urban interface

This paper describes micro‐economic models of land use change applicable to the rural‐urban interface in the US. Use of a spatially explicit micro‐level modelling approach permits the analysis of regional patterns of land use as the aggregate outcomes of many, disparate individual land use decisions distributed across space. In contrast to the models featured by Nelson and Geoghegan, we focus on models that require spatially articulated data on parcel‐level land use changes through time. In characterising the spatially disaggregated models, we highlight issues uniquely related to the management and generation of spatial data and the estimation of micro‐level spatial models.     

Land use and income diversification: comparing traditional and colonist populations in the Brazilian Amazon

This article compares and analyzes land use and income diversification among two distinct groups of farmers in the Brazilian Amazon: recent colonists in Ouro Preto do Oeste, Rondônia, and traditional long‐term residents along the Tapajós River, Pará. We investigate the hypothesis that farmers who diversify their cash income sources clear less forest on an annual basis, and we compare these livelihood choices across colonist and traditional populations. In particular, we develop a conceptual model based on the household production framework and use econometric models to identify determinants of diversification and forest clearing. We find that diversification of agricultural cash crops is negatively correlated with forest clearing by colonists, providing limited evidence for the hypothesis. Other significant covariates of diversification and forest clearing include cash income levels, stage in family life cycle, cattle ownership, and chemical inputs. Differences in these variables, and differences in household response to these variables, explain variation in diversification and forest clearing across the two populations.     

Spatial Economic Analysis in Data‐Rich Environments

Controlling for spatial effects in micro‐level studies of consumer and producer behaviour necessitates a range of analytical modifications. These range from modest changes in data collection and the definition of variables to dramatic changes in the modelling of consumer and producer decision‐making. This paper discusses conceptual, empirical and data issues involved in modelling the spatial aspects of economic behaviour in data‐rich environments. Attention is given to established and emerging agricultural economic applications of spatial data and spatial econometric methods at the micro‐scale. Recent applications of individual and household data are featured, including models of land‐use change at the urban–rural interface, agricultural land values, and technological change and technology adoption.     

我国土地利用的经济效益初探

研究目的对我国土地资源结构布局与土地利用的经济效益进行探索性的分析研究。研究方法比较分析法。研究结果：我国土地资源结构布局及经济效益的比较分析说明，人多地少或人均耕地并不是经济发展的严重制约因素．土地的经济生产力是形成东西部差距的主导因素，而对土地的投入和产业结构或用地结构的差异是最关键的。     

ECONOMIC ASPECTS OF LAND DEGRADATION IN AUSTRALIA

In this article an overview of several economic aspects of land degradation in Australia is presented. The economic rationale for government intervention in land management decisions relating to degradation is explored. Some potential sources of inefficient private land use decisions are identified. However, there are significant difficulties in designing policies which will result in resource allocation decisions superior to market outcomes.     

Life cycle models of women's body mass index and probability of being obese: Evidence from panel data

The objective of this paper is to develop a multiperiod, finite‐life, life cycle models of household decisions on food, leisure, and health (body mass index [BMI] or being obese) and to estimate econometric versions of these models treating SNAP (Supplemental Nutrition Assistance Program) participation as endogenous. A key insight from the economic models is that households allocate their wealth over the multiperiod life cycle to equalize the marginal utility of wealth in each period. The observations for this study are a balanced panel of over 1,600 women from the National Longitudinal Survey of Youth, 1979 Cohort (NLSY79). We focus on the 20‐year period starting in 1986, when SNAP data first became available. Women of all ages are included in the study because at the beginning of adulthood women cannot accurately predict over their life cycle labor and marriage market and health shocks that can thrust them into an economic position where they would qualify for SNAP. New findings include that a woman's household SNAP participation with or without updating for last periods health status and higher local dairy product prices reduce significantly her BMI and probability of being obese.     

中国建设用地集约利用变化及分区管控研究

研究目的:基于经济发展和资源禀赋的分异划分建设用地管控分区,并据此提出区域差别化的管控策略。研究方法:运用全国省际面板数据构建计量模型分析不同经济发展水平下建设用地集约度的变化规律,通过资源禀赋指标与经济发展指标叠加划分建设用地集约管控区域;再运用因子分析法研究集约度变化的影响因素及其区域差异。研究结果:(1)建设用地集约度增加量与经济发展水平符合库兹涅茨曲线规律,随着经济发展水平的提高,集约度的增加量先呈上升趋势,当人均GDP超过69251元/人后转为下降趋势,建设用地集约利用水平最终将趋于稳定。(2)全国可划分为4类建设用地管控区域:优化调整区、重点发展区、适度发展区和内涵挖潜区。(3)建设用地利用集约度变化的主要影响因素有社会经济、资源供给、产业结构和配置方式4个方面。研究结论:应根据区域影响因子作用差异,从增量供给、门槛约束、存量挖潜、市场建设等方面制定区域差别化的集约管控策略。     

Introduction to the special issue on spatial analysis for agricultural economists

The goal of this special issue is to introduce agricultural economists to new analytical approaches involving spatial data. This paper provides a brief history of the special issue and an introduction to von Thünen's model of the determinants of land use and rent that underlies all spatial analysis.     

Are Agricultural and Developable Land Prices Governed by the Same Spatial Rules? The Case of Belgium

In this paper, we test whether farmland and developable land prices are governed by the same determinants (mainly urbanization). We estimate here quasi‐identical econometric equations for both agricultural and developable land markets, instead of the conventional approach of adding variables that capture the urban influence. Using data aggregated at the level of the 589 municipalities of a highly urbanized country (Belgium), we found that the same determinants enter these equations, with slight differences in parameter values: land prices decrease with distance to central business district (developable land: –2.7% per km; farmland: –2.5% per km), and increase with the population of the commune as well as with its demographic growth and with the average income of its inhabitants.     

Measuring soil quality dynamics A role for economists, and implications for economic analysis

Measuring soil quality is extremely difficult, yet it has clear economic importance. In particular, there is a great deal of empirical interest in the dynamics of soil quality evolution when land managers respond to policies and other incentives. Yet current methodologies for measuring changes in agricultural land quality are largely static and rely heavily either on incomplete measures such as proxy variables, or ad hoc indexes of selected soil characteristics. Moreover, much empirical work relies on static econometric techniques or simulation models. In this paper, we develop a means to infer soil quality changes from input and output data using a dynamic production function model. Using data from field experiments, we estimate the model in a way that allows the recovery of a dynamic measure of soil quality whose evolution depends on variations in management practices. Our methodology and findings will help provide firmer empirical foundations for analyses of the economic implications of land degradation and the soil quality implications of agricultural policies.     

Agriculture and climate change in global scenarios: why don't the models agree

Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (temperature, solar radiation available to the plant, and precipitation). Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes such as prices, production, and trade arising from differences in model inputs and model specification. This article presents climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By harmonizing key drivers that include climate change effects, differences in model outcomes were reduced. The particular choice of climate change drivers for this comparison activity results in large and negative productivity effects. All models respond with higher prices. Producer behavior differs by model with some emphasizing area response and others yield response. Demand response is least important. The differences reflect both differences in model specification and perspectives on the future. The results from this study highlight the need to more fully compare the deep model parameters, to generate a call for a combination of econometric and validation studies to narrow the degree of uncertainty and variability in these parameters and to move to Monte Carlo type simulations to better map the contours of economic uncertainty.     

Biophysical and socioeconomic determinants of tea expansion: Apportioning their relative importance for sustainable land use policy

Tea expansion, a typical process of regional land use and cover change (LUCC), has raised great concerns on regional sustainability. In this regard, exploring the determinants of tea expansion should provide critical implications for land use policy. It has been widely recognized that LUCC interacts nonlinearly with a set of determinants and their feedbacks should be rather complex. Policy makers are now facing the challenge to identify, apportion, and compare the determinants of regional tea expansion for designing more targeted political intervenes. Our paper utilizes a robust tool, the random forest (RF) regression in particular, to explore the determinants of tea expansion across two periods (1985–2007 and 2007–2016) in Anji County, a typical region of tea production in subtropical China. More specifically, tea is extracted from Landsat imageries and total tea cultivated area acts as the dependent variable. Exploratory variables include 38 potential determinants and these determinants are divided into two categories (biophysical and socioeconomic) at two levels (pixel and village). We obtain some similar findings, though the relative importance of determinants varies with the two periods. In general, biophysical determinants (e.g., topography, soil type, land use in the neighborhood) present greater relative importance than the socioeconomic determinants in both periods. In period 1985–2007, biophysical determinants at pixel level are more essential in governing tea expansion. In period 2007–2016, the relative importance of pixel level biophysical determinants is comparable with that of the village level determinants. Comparisons of the two periods indicate that relative importance of soil type and socioeconomic proximity becomes greater in period 2007–2016, while that of the total employees and non-agricultural population proportion becomes lower. Partial dependency plots are further drawn to visualize the marginal effect of each determinant. We finally propose three options for land use policy towards sustainability. Our study demonstrates that the RF regression is efficient for policy makers to understand the determinants of tea expansion with a nonlinear and complex nature.     

The incentives of China's urban land finance

Land grant premiums and land tax revenues have become two major sources of fiscal revenue for city governments in China. This type of fiscal revenue strategy for city governments is generally referred to as “land finance”, and it has drawn increasing research attention in recent years. This paper explores the institutional causes of the “land finance” strategy of city governments in China. We first analyze the institutional foundation of “land finance” (including China's urban land use system and land expropriation system). We then propose two hypotheses about the institutional causes of “land finance”. The first hypothesis is that the current system of fiscal decentralization is a major reason city governments choose the “land finance” fiscal strategy. The second hypothesis is that under the current personnel control system, which uses local economic performance as the most important indicator for evaluating local government officials, the competition between city governments to promote local economic growth is another major reason city governments choose the “land finance” fiscal strategy. We test the hypotheses by estimating econometric models using data for 31 provincial-level regions for the period 1999–2008. The empirical results suggest that fiscal decentralization and competition between city governments to promote economic growth are two major causes of “land finance”.     

Capturing household-level spatial influence in agricultural management using random effects regression

Data on agricultural and natural resource management typically have spatial patterns related to the landscapes from which they came. Consequently, econometric models designed to explain the determinants of humans' natural resource management practices or their outcomes often have spatial structure that can bring bias or inefficiency to parameter estimates. Although econometric tools are available to correct for spatial structure, such tools are largely lacking for use with discrete dependent variable models. While one obvious solution would be to develop the necessary tools, an alternative is to identify conditions under which spatial dependency can be managed effectively without formal spatial autoregressive models. This study examines conditions under which spatial structure corresponds closely to defined agro‐ecological zones, making it possible to model spatial effects by random effects regression. Using household survey data sampled along agro‐ecological zone strata, this article develops two models of links between farmer assets and agricultural natural resource degradation in southern Peru. The first stage model looks at determinants of crop yield loss over time (an index of soil productivity), while the second stage model looks at determinants of the extent of fallow cycles in crop rotation, a key agricultural practice reducing crop yield loss. Diagnostic statistics for spatial dependency reveal spatial structure, particularly in the fallow model. This spatial dependency is eliminated in the ordinary least squares (OLS) models by inclusion of the agro‐ecological zone random effects. In the spatially dependent fallow model, comparison of coefficient estimates between OLS and the spatial autoregressive maximum likelihood models showed OLS with random effects to give virtually identical results to the spatial autoregressive models, making the latter unnecessary. These results show that spatial structure in natural resource management models can sometimes be captured by zonal variables. When this occurs, random effects regression can largely eliminate spatial dependency. A necessary precondition for this approach with household survey data is prior sample stratification according to landscape characteristics. Where random effects models can effectively capture spatial structure, they may also offer analysts greater flexibility in analyzing models with limited dependent variables.     

The dynamics of land-cover change in western Honduras: exploring spatial and temporal complexity

This paper presents an econometric analysis of land‐cover change in western Honduras. Ground‐truthed satellite image analysis indicates that between 1987 and 1996, net forest regrowth occurred in the 1015 km² study region. While some forest regrowth can be attributed to a 1987 ban on logging, the area of forest regrowth greatly exceeds that of previously clear‐cut areas. Further, new area was also deforested between 1987 and 1996. Thus, the observed land‐cover changes most likely represent a complex mosaic of changing land‐use patterns across time and space. Using satellite imagery from 1987, 1991 and 1996, we estimate a series of models, including binary probit models for each date, and a random‐effects probit model using panel techniques. We also experiment with spatial sampling schemes designed to reduce residual spatial autocorrelation, and qualitatively compare the impact of spatial sampling on model accuracy. Lastly, we find that changes in relative prices, infrastructure improvement, and topography are all significantly related to changing land‐cover patterns.     

Integrating expert knowledge and quantitative information for mapping cropping systems

Scenarios are accepted as useful tools for assessing economic and environmental impacts of land use changes, for involving stakeholders in environmental issues and thus for decision-making. In most studies involving comparison and evaluation of scenarios, a reference situation from which different scenarios will be developed is defined. When dealing with rural areas, scenarios, and thus reference situation, should not only consider land use but also cropping systems. The usual lack of data concerning cropping system distribution in a given region can be overcome by the use of expert knowledge. In this paper, we present a procedure for the spatial allocation of cropping systems that integrates expert knowledge and quantitative data. The aim of this procedure is to develop a reference situation that makes explicit the link between cropping systems and the factors responsible for their geographical location (location factors).     

Protecting Watershed Ecosystems through Targeted Local Land Use Policies

Land use change is the most pervasive force driving the degradation of watershed ecosystems. This article combines an econometric model of land use choice with models of watershed health indicators to examine the effects of land use policies on watershed ecosystems through their effect on land use. Our results suggest that incentive-based land use policies and property acquisition programs can have relatively large positive impacts on watershed health, while policies that change the returns to land use are less effective. The results suggest that there is potential for targeting these policies because their impacts vary across watersheds with different land use mixes.     

Management flexibility,price uncertainty and the adoption of carbon forestry

A price on carbon has the potential to drive significant land use change through reforestation. Understanding the likely locations and extent of these changes is therefore a key focus for researchers and policy makers. Models of reforestation based on net present values (NPV) typically compare the economic returns of carbon forestry to alternative land uses. However, these models often neglect the impact of uncertainty. Two sources of uncertainty highly relevant to carbon forestry are the opportunity cost of the land on which the trees are established (i.e. future returns from alternative land uses) and carbon prices. In addition to foregoing the current land use, a landowner making a permanent land use change such as carbon forestry is also giving up the opportunity to change management in the future, for example by changing crop mix in response to commodity price changes. We develop a Monte Carlo model to demonstrate the value of management flexibility, based on a case study property in Australia. While in the absence of management flexibility carbon forestry is more profitable than the current land use, under uncertain future commodity prices it is less attractive to a landowner. We go on to show that, even if the returns from carbon exceed those from more flexible agricultural land use, uncertainty over future carbon prices is likely to delay the adoption of carbon forestry. Overall the models presented in this paper demonstrate that the adoption of carbon forestry is likely to be substantially lower, and slower, than models based on static values would suggest.