Economic impacts of alternative greenspace configurations in fast growing cities: The case of Greater Beijing

Abstract

Chinese

Greenspaces at the city scale, like greenbelts, green-wedges or green-grids, have become well-known instruments for shaping urban economic activity and land use. The economic impacts of such instruments are complex and hard to measure. This article addresses one of the rarely studied problems of measuring the economic impacts of alternative greenspace configurations in fast growing cities. In such cities, there is an uncertain basis for making such greenspace related decisions, for example the assumptions about the cities’ total population and economic activity. Decision makers have few tools to measure and predict the potential economic costs and benefits of alternative greenspace configurations. We present a new model that allows tracking over time of both non-divisible land use decisions and a multitude of gradual adaptations made by businesses and consumers. The model is applied to Greater Beijing, one of the fast growing cities that is actively exploring alternative greenspace configurations to control urban expansion. The modelling results suggest that compared with the trend-development scenario of no greenspace intervention, a strict greenbelt would decrease the overall consumer surplus in Beijing by US$3.3 billion, while an adaptive mix of green-wedges and green-grid would increase consumer surplus by US$3.6 billion per year in 2030. The adaptive configuration also reduces car journeys by 11% in Beijing. More generally, modelling results show that a flexible design of strategic greenspaces and careful siting of new development around metro stations within the designated greenspaces could benefit consumers and promote sustainable travel.

Keywords

Greater Beijing greenspace Land Use Transport Interaction (LUTI)recursive spatial equilibrium urban modelling

Introduction

Designating large urban greenspaces in planning is a frequently adopted means of land use control. The preserved greenspaces are considered key instruments for protecting the eco-system, creating amenity and recreational spaces and preventing undesirable expansion. Urban greenbelt, green-wedges and green-grid are alternative configurations that have been implemented. However, their economic impacts are complex and hard to measure. This is because large urban greenspaces will have impacts on multiple urban markets, for example the land market, labour market, product market and transport market. The impacts of greenspaces on each market are hard to quantify without considering the complex and concurrent interactions among these markets. Meanwhile, large urban greenspaces will have enduring impacts on a city’s economic wellbeing. Such impacts are hard to trace over time because of the changing historic background conditions.

The greenbelt is the most common strategic greenspace configuration and is implemented in a large number of cities (Amati, 2008; Buxton and Goodman, 2003; Carter-Whitney and Esakin, 2010; Kim and Kim, 2008; Okata and Murayama, 2011; Tang et al., 2007; Staley et al., 1999). It is considered crucial for shaping compact urban form, stopping urban sprawl (Pendall et al., 2002; Staley et al., 1999) and reducing Vehicle-Miles-Travelled (VMT) (Ewing and Cervero, 2010, 2017). However, for fast growing cities where development demands keep growing beyond existing masterplans, which is often the case, greenbelts could look, at least to some, anachronistic. In cities where urban growth has exceeded their planned targets, greenbelts have been criticised for increasing land value in the existing city centre (Ball et al., 2014), encouraging car travel (Rodriguez et al., 2006) and increasing urban congestion (Freestone, 2002).

The green-wedges policy is an alternative configuration to the greenbelt and has been proposed or implemented in several cities (Cambridge Futures, 2000; Knowles, 2012). The idea is to allow developments to radiate from the city centre into the designated greenbelt often along public transport corridors, normally at a relatively high net density, such that urban expansion can better integrate with major public investments like rail or metro. The green-grid is another alternative. The idea is to take a bite out of the greenbelt and transform that into a carefully designed plot for development (Rudlin and Falk, 2014). Also, the green-grid idea seeks to connect small pieces of existing greenfield land like small parks and orchards.

Alternative greenspace configurations take a long time to shape. Their governance tends to prefer stability and permanency in their designations. Once they are established, it is very hard to reverse the land use patterns. In fast growing cities, this is problematic. This calls for stronger evidence that can better anticipate different rates of urban expansion. Such a requirement is much tougher than in developed countries with slower growing cities. However, there are few tools for policy makers to assess alternative greenspace plans on a consistent basis.

For cities that are reviewing greenbelts and/or initiating alternative configurations, a rigorous tool is needed to measure and predict the potential policy impacts, so that the cost of implementing an unpredictable policy may be avoided. This prompts us to develop a new tool for predicting and comparing the performance of alternatives for fast expanding cities. We aim to answer the following questions: What are the short-term and long-term economic effects of different greenspace configurations? Which configuration performs better in terms of economic efficiency and consumers’ wellbeing?

A review of existing methods

Empirical comparisons were conducted to evaluate the performance of established greenspaces in cities in developed economies (Giuliano and Narayan, 2003; Jun, 2004; Siedentop et al., 2016; Woo and Guldmann, 2011). Such studies are based on well-documented data for cross-sectional and cross-city comparisons. For example, Siedentop et al. (2016) compared the land coverage changes of four regions with different levels of greenbelt controls over a decade. However, for cities in emerging economies, such comparisons were hindered by the difficulty in acquiring data.

Partial regression models are commonly used to identify specific economic effects of greenspaces on a single urban market. For example, Lee and Linneman (1998) and Correll et al. (1978) identified amenity value changes as probable consequences from greenbelts or urban growth boundaries. Nelson (1986, 1988), Knaap (1985) and Ball et al. (2014) identified land and house price differentials due to land supply constraints. Such studies generally do not specify a model for the housing market in an equilibrium state.

In addition to partial models, feedback and interactions amongst urban markets were established in spatial interaction models, such as in Lee and Fujita (1997), Bae and Jun (2003) and Jun (2011). Lee and Fujita (1997) modelled the interactions among residents, landowners and governments subject to greenbelt locations, and used the Herbert-Stevens model for the optimal provision of a greenbelt. Jun (2011) established a random utility-based input–output model with an endogenous land market to test the effects of Seoul’s greenbelt on the spatial distribution of population and jobs.

Existing studies have pointed out that greenspaces will have impacts on multiple urban markets, and due to the multitude of concurrent changes of factors in the whole urban system, it is empirically impossible to isolate one urban market from the others (Wegener, 2004). Therefore, whole-city models are needed to test the impacts of greenspaces and to reveal the reciprocal connections between transport and land use (Jun, 2004; Rodriguez et al., 2006; Woo and Guldmann, 2011).

Moreover, for modelling fast growing cities, in addition to evaluating policy performance at a particular cross-section or end-state, the time dimension and path dependency of urban development have become important. This is particularly the case for cities that have to modify their growth plans significantly during fast growing phases.

In this light, we develop a new analytical model which can address the interactions amongst various urban markets on a whole-city scale, and the dynamic nature of urban transformations over time. The model will predict the economic impacts of greenspaces on four major urban markets: the day-to-day adaptions of the transport market, the short-to-medium-term impacts on labour and product markets and the long-term evolution of housing and business floorspace.

The theoretical model

The analytical model is a new variant of the Land Use and Transport Interaction (LUTI) model, which combines the principles of spatial equilibrium with non-equilibrium urban dynamics. LUTI models have been applied in cities around the world to provide an evidence base for planning decisions. For systematic reviews of LUTI models in application, see Batty (2009), Wegener (2004, 2014) and Iacono et al. (2008). For the LUTI model tradition that we follow in this article, long-term and repeated applications in cities such as Bilbao in Spain, Santiago in Chile and Cambridge and London in the UK have been effective in supporting the formulation of policies in the last 40 years (Research Excellence Framework, 2014).

Traditionally, LUTI models assume an equilibrium between supply and demand in the land use and transport markets (Anas and Liu, 2007; Echenique, 2011; Williams, 1994), while recent developments of LUTI models consider the different speeds of urban changes and concentrate on their outcomes over time (Simmonds et al., 2013; Wegener, 2004). Based on these substantial contributions, we select a combined equilibrium-recursive structure (Jin et al., 2013) for our model representation, in which the end state of one time period serves as the initial state of the subsequent time period: the equilibrium component captures short-to-medium-term adaptions of travel and location choices, while the recursive component follows up long-term development trajectories under different greenspace configurations.

The equilibrium component is developed based on a generic LUTI framework. The land use markets are further developed into two sub-models (see Figure 1; equations are presented in Supplementary Material 1): the Spatial Equilibrium Model, which explores the employment-related urban activities; and the Non-commuting Travel Demand Model, which explores the non-commuting urban activities.

Figure 1.

Modelling structure.

The Spatial Equilibrium Model (built in Matlab scripts following the RELU-TRAN structure) predicts the employment and residential locations under a simultaneous equilibrium in the production market, labour market and real estate market. The core of this sub-model includes a hybrid Cobb-Douglas Constant Elasticity of Substitution (CES) function for producer choices for production inputs and consumer choices for consumption (Anas and Liu, 2007; Anas and Rhee, 2006; Jin et al., 2013). It predicts employment-residence locations based on a logit form discrete choice model, which facilitates the non-commuting activity generation and transport demand estimation. It also offers the starting point of the development trajectories and defines the inertias and trends for the next decade.

The Non-commuting Travel Demand Model (built in the MEPLAN-LUS module) takes the prediction of employment-residence locations from the Spatial Equilibrium Model, and predicts locations of non-commuting urban activities based on an input–output social accounting matrix and market constraints. It generates non-commuting travel demand for the transport simulation.

The transport market is represented by a Strategic Transport Model, which is a trip-based model. This model is built in the MEPLAN-TAS module. It takes the travel demands, namely trip ends, from the two land use sub-models, splits traffic flows into travel modes and assigns them on the transport network. By doing this, it gives the travel disutilities between origin–destination (OD) zone pairs back to the two land use sub-models, which is the bridge for land use and transport interaction.

The Spatial Equilibrium Model, Non-commuting Travel Demand Model and Strategic Transport Model form the equilibrium component and represent one cross-sectional year of simulation. The Recursive Dynamic Model (built in Matlab script, following the MEPLAN-LUS structure) links several cross-sectional years and updates the floorspace supply and transport network for the next cross-sectional year, based on endogenous outputs from the spatial equilibrium component that represents market inertia and exogenous policy interventions.

The modelling package starts with a cross-sectional base year run. In the base year, the model runs under calibration mode. It receives two main inputs: the quantities of housing and business floorspace, and the travel disutilities between all OD zone pairs. Model predictions (numbers of jobs, residents, production value, demands for floorspace, rents and wages) are compared with known zonal quantities and prices from published statistics, such as censuses and travel surveys, to refine the parameters. The parameters are validated and re-estimated. The validation requires a second observed cross-sectional year to test the suitability of the calibrated parameters. After validation, the parameters are then used for prediction.

The model outputs show the zonal average economic productivity, household utility and travel behaviour by socio-economic group under alternative greenspace configurations. The main outputs include zonal population and employment numbers, rents, household utility by socio-economic group, the floorspace stock, travel times, distances and costs by socio-economic group and by travel purpose and the travel mode share. The outputs provide a consistent basis for comparing economic impacts of alternative greenspace configurations.

Model application: Which greenspace configuration for Greater Beijing in 2030?

We test the theoretical model on one of the fastest growing cities – Greater Beijing. It is a typical example of a fast growing city which intends to establish strategic greenspaces to achieve environmental benefits and control undesirable urban expansion.

Policy context and geographical specification

Beijing introduced its first greenbelt in 1994 in order to stop the successive urban expansion wave. The second greenbelt was introduced in 2003 and emphasised the latest Beijing Masterplan of 2016–2035. Studies have shown that Beijing’s greenbelt policies increased tree canopy cover (Yang and Zhou, 2007), encouraged tree planting on village brownfield (Tan, 2008), preserved the continuity of large pieces of greenspaces (Gan, 2012) and safeguarded water bodies and forest (Han and Long, 2010).

Greenbelts are well-known for their environmental benefits, but the promoters were frustrated by the difficulties in policy implementation. In fact, data show that the percentage of greenfield land remaining from the proposed first greenbelt is less than 11% (Wang, 2015). In the second greenbelt, land that is available for greenery reduced from 412 km² in 2002 to 163 km² in 2007 (Beijing Municipal Commission of Urban Planning, 2007). The setback is attributed to weak planning regulation (Han and Long, 2010; Yang, 2008). However, there is a lack of research and a gap in understanding and measuring the economic impacts of greenbelts. In the revision of current greenbelt policy, planners have proposed an alternative configuration that breaks greenbelt into green-wedges and green-grid (Beijing Municipal Government, 2017; Beijing Urban Planning and Design Institute, 2007).

In order to mitigate the overconcentration of business activities and population in Beijing, the national government has launched a Master Plan of Beijing-Tianjin-Hebei Integrated Development in 2015 (Central Political Bureau of China and The State Council, 2015). This plan emphasises that regional integration is crucial for tackling the perceived overcrowding in Beijing. Expressways and high-speed railway projects have been proposed to facilitate the redistribution of economic activities and population, but there are few tools to understand and measure the effects of such strategic plans.

In order to include all relevant land use activities and transport flows within the megacity region, both for the present spatial extent and for expected future expansion, our Greater Beijing Model includes Beijing Municipality, Tianjin Municipality and Hebei Province (see Figure 2). The model zoning at the level of local cities and counties allows the effects of strategic greenspace scenarios to be assessed at a level that is consistent with local governance for investment and regulation, as well as for region-wide coordination.

Figure 2.

Geographical extent (left) and zone categories in Beijing (right).

The region is divided into 209 zones according to the existing administrative boundaries and transport links. Beijing Municipality is divided into 130 zones with a detailed road network. Out of the 130 zones, 28 zones at Beijing’s urban fringe are selected as greenbelt zones for policy test purposes. A greenbelt zone may turn into a built-up zone or a zone with multiple Transit Oriented Development (TOD) nodes, or may be preserved as the greenbelt. For a zone with TOD nodes, development takes place around each station in relatively high net density, and both housing and business floorspace are allowed following the Density-Diversity-Design principle (Ewing and Cervero, 2010). These areas are expected to yield low vehicle ownership, high transit and walking mode shares on work trips and short drive times to work (Cao and Chatman, 2016; Cervero, 2015; Ewing and Cervero, 2010, 2017).

We further classify the 130 zones in Beijing Municipality into six categories: 1) the central city, 2) the inner city, 3) the greenbelt zones, 4) the new towns, 5) the far suburb, and 6) the ecological protection area (EPA).

Greenspace scenarios

Based on the existing plans and the historical development trends, we classify three greenspace configurations from 2010 to 2030: 1) Beijing’s past expansion has pointed to the concentric growth as the default Trend development scenario, which means no specific greenspace intervention will be implemented. 2) Despite the fragmented remaining greenspaces, there are appeals for no more development in the designated second greenbelt (Beijing Municipal Commission of Urban Planning, 2012; Beijing Municipal Government, 2008). Therefore, the Greenbelt is the second scenario. 3) As the mixed greenspace configuration has been proposed in the latest masterplan, we test it as the third scenario, called Green-grid. In this scenario, parts of the greenbelt will gradually be built up, and the ring-shaped greenbelt will evolve into wedges, then into a grid over time. The selected built-up areas are those with metro or train services. Such built-up areas are TOD nodes. Figure 3 illustrates the three scenarios.

Figure 3.

Scenarios of greenspace configurations.

The three scenarios are based on the same projections of regional macroeconomic and demographic growth. That is to say, they share the same overall assumptions of total number of population, same regional floorspace stock and same future road and metro networks as set out in the government’s development and infrastructure plan. Metro capacity and fares stay the same as in the base year across scenarios, because the service frequency is already very high and the fares are very low. The scenario variations are represented at: 1) Where the floorspace growth takes place; 2) How close people live to the transport network in the greenbelt zones.

Regarding the first variation, the total floorspace constraints remain the same across scenarios. This makes it possible for a consistent comparison between the scenarios for the level of floorspace rents. Zonal floorspace constraints are defined endogenously in the model. The floorspace growth is divided into natural growth and added growth. Natural growth is the spontaneous expansion through extension of the existing buildings or infilling development in built-up areas. It is distributed proportionally to the existing stock size, unless defined as zero in greenbelt zones (see Table 1). Added growth is designed to reflect the land allocation and policy interventions. It is distributed endogenously in the Recursive Dynamic Model according to price levels in the previous decade (see Equations 9 and 10 for the calculation of floorspace supply). We follow the assumption from Jin et al. (2017) and estimate that natural growth accounts for 50% of the projected total growth. This ratio can be modified by the modeller.

Table 1.

Locations of floorspace development.

Scenarios	Locations of floorspace development							Proximity to transport network
	Centre	Near suburb	Greenbelt zones	(TOD nodes)	New towns	Far suburb	EPA	Road access link length	Station access link length
1 Trend	×	√	√	N/A	√	√	×	93% r	86% r
2 Greenbelt	×	√	0	N/A	√	√	×	100% r	114% r
3 Green-grid	×	√	0	√+	√	√	×	Same as scenario 2except for TOD nodes, where
3 Green-grid	×	√	0	√+	√	√	×	96% r	58% r

Notes: √ denotes that both natural growth and added growth are allowed. × denotes that only natural growth is allowed; no added growth. 0 denotes that no growth is allowed. √+ denotes that both natural growth and added growth are allowed; meanwhile, added growth is promoted. r is the zonal radius, which is the arithmetical average distance from the geometrical centroid to all the perimeter vertices.

Table 2.

Aggregate modelling results on the Beijing municipal level.

	1 Trend	2 Greenbelt *	3 Green-grid *
Employed residents (thousand people)	18,887	–6%	–2%
Employed workers (thousand people)	18,980	–6%	–2%
Housing floorspace supply (million m²)	1169	–11%	0%
Housing floorspace within the originally designated greenbelt zones in 2030 (million m²)	330	–42%	–1%
Business floorspace supply (million m²)	461	–4%	0%
Business floorspace within the originally designated greenbelt zones in 2030 (million m²)	107	–41%	9%
Average housing rent (US$/m²/year)	194	7%	–1%
Average business floorspace rent (US$ /m²/year)	81	3%	–2%
Total production in quantity of composite goods (millions)	5687	–3%	–2%

Notes: *Percentage of change to Trend.

Regarding the second variation, the transport proximity is estimated in a case-by-case manner only in greenbelt zones according to the scenario. Access links are built from the zonal centroid to each metro station and major road junction to represent the transport proximity. The average access length is calculated using a conceptual circular city model (Holroyd, 1966) and is validated through Beijing’s data (Ma, 2017).

Model calibration and specification for forecasts

A well-calibrated base year is fundamental for the accuracy of future predictions. The predictive capability of LUTI models is assessed through whether a model can reproduce the current (or historical) situation as represented by the data on that situation (Wilson, 1998). The Greater Beijing model is established based on cross-sectional data for three decades, namely the 1990s, 2000s and 2010s. The model is calibrated for year 2010, and validated through reproducing year 1990 and 2000. The multiple cross-section validation ensures that the model is well structured and calibrated for predicting medium- to long-term trends (Wan and Jin, 2017). Parameters and data inputs for calibration are reported in Supplementary Material 2.

We then run the model from year 2010 to year 2030,¹ based on the following segmentations: three social-economic groups (high, medium, low) segmented according to their income levels and jobs; five travel modes (car, bus, walk, cycle, metro/rail); four travel purposes (commuting, education, business, other) in line with the transport survey in Beijing.

Results

Aggregate modelling results on the Beijing municipal level

The aggregate level modelling results in Table 2 show that greenspace interventions will have a wider impact beyond Beijing Municipality on to the entire region. Compared with the Trend scenario, both Greenbelt and Green-grid stimulate a relocation of population and jobs from Beijing to its surrounding areas. The magnitude of the relocation is more obvious with the implementation of the strict greenbelt. The Greenbelt scenario has the least housing provision. The shortage of floorspace pushes rent up by 7% for housing and 3% for business floorspace, despite the shrinking demands due to the decrease of population and employment in Beijing.

Localised results

Compared with Trend, the greenbelt zones lose more than half of the residents and jobs in the Greenbelt scenario (see Figure 4). People move from the greenbelt to the inner city, instead of to new towns in the far suburb. While people concentrate in the centre, there is no added employment. Jobs are pushed further from the greenbelt to the wider south-east transport corridor towards Tianjin.

Figure 4.

Distribution of employed residents and workers in Trend, and the changes in Greenbelt/Green-grid compared with Trend.

The impacts of Green-grid are relatively local within the urban fringe. With the sheer drop of the number of residents in the preserved greenbelt zones, TOD nodes become popular. Number of jobs also increases in TOD nodes to achieve work–home balance.

Figure 5 shows the variations in housing floorspace supply and rent. Under the greenbelt restriction, the floorspace supply in the greenbelt is remarkably low. Meanwhile, because fewer people live in the greenbelt, the shrinking demand brings down housing rent by about 10%. Housing rent in the rest of Beijing goes up in general and the rent in the city centre increases by 13%. Note that the amenity value of the greenbelt is not modelled explicitly; instead, it is included in the residual attractiveness $E_{f i j}$ (see Supplementary Material 1, Equation 2). The residuals are empirically calibrated for 2010, so any potential increase of amenity value from 2010 to 2030 may be underestimated, and thus the magnitude of rent decrease in greenbelt zones may be overestimated. In Green-grid, the TOD nodes become popular primarily because of the sufficient floorspace supply. Most of the nodes see a more than 50% increase in housing supply and rent increases accordingly because of the rising demand.

Figure 5.

Housing floorspace density and rent in Trend, and the changes in Greenbelt/Green-grid compared with Trend.

Table 3 reports the transport modelling results. On the regional level, a reduction of VMT can be observed in the Green-grid scenario. For Beijing Municipality and its urban fringe, mode choice shifts from car to non-vehicle and public transport can also be observed. The overall mode choice shift is due to the work–home balance in TOD nodes. As people work locally, intra-zonal commuting is promoted where the car is uneconomic: intra-zonal journeys are in general shorter than inter-zonal journeys, so off-network travel time and parking fees ( $p_{j m}^{t}$ in Equation 8) account for a larger proportion in the generalised travel costs of car journeys, which reduces the advantage of cars compared with other modes. Additionally, for TOD nodes, development takes place around stations so that the access time to public transport is shorter, which promotes metro mode share.

Table 3.

Main transport modelling results.

	1 Trend	2 Greenbelt	3 Green-grid
Vehicle miles travelled in the Beijing–Tianjin–Hebei region
Passenger miles travelled per person per year	6994	6989	6893
Vehicle miles travelled per person per year	3282	3247	3148
Commuting trips in Beijing
Average commuting distance	13.6	13.3	13.1
Trips by car (thousand trips)	9108 (58%)	8443 (57%)	8097 (52%)
Trips by non-vehicle modes (thousand trips)	1864 (12%)	1795 (12%)	2126 (14%)
Trips by public transport (thousand trips)	4753 (30%)	4546 (31%)	5191 (34%)
Commuting trips in greenbelt zones
Trips by car (thousand trips)	2552 (60%)	1365 (62%)	1686 (42%)
Trips by non-vehicle modes (thousand trips)	489 (12%)	221 (10%)	766 (19%)
Trips by public transport (thousand trips)	1204 (28%)	631 (28%)	1541 (39%)

Figure 6 reports variations in consumption utility among scenarios. Utility is determined by two factors: how much floorspace a household can afford to rent and how many goods they can buy. Under the Trend scenario, due to high rents, the utility level in Beijing (6.40) is generally lower than in its surrounding areas (6.75 regional average), and the inner city is even lower due to the high price level. The Greenbelt scenario fails to improve the consumption utility for most zones, as the average utility drops to 6.38. The designated greenbelt area is an exception. With the decrease in population and the drop in rent, the greenbelt turns into an affordable area at the cost of making the rest of Beijing more expensive. In Green-grid, consumption utility witnesses an overall increase in Beijing to 6.42, with the exceptions of a limited number of TOD nodes and suburban zones. Under Green-grid, the total number of residents decreases by 2% in Beijing while housing supply remains the same. The shrinking demand brings rent down by 1%. Moreover, the average commuting distance decreases from 13.6 km in Trend to 13.1 km, mainly because more people work locally, and the distance to stations is shorter.

Figure 6.

Utility level in Trend, and the changes in Greenbelt/Green-grid compared with Trend.

When we convert the utility into monetary terms by Equation 12, the drop of utility in Greenbelt equals a loss of US$349 per employed resident per year, which equals US$3.3 billion for the whole municipality. This drop accounts for 1.56% of total household disposable income. In Green-grid, the increase of utility equals US$385 per employed resident, which means Beijing gains US$3.6 billion. This increase accounts for 1.72% of total household disposable income.

Discussions and conclusions

In the context of the policy goal for regional integration, the modelling results highlight the significant geographical variations of impacts. A strict control of the greenbelt will relocate 6% of the population from Beijing to Tianjin and Hebei, which at face value may be considered a positive contribution to relieving overcrowding in Beijing. However, on closer look, under the current inter-city transport plan, the greenbelt will concentrate more employed residents (0.55 million) in Beijing’s city centre. In this sense, the strict greenbelt may not relieve overcrowding where the growth pressures are the highest, and could even exacerbate it.

The model results help to pinpoint where the growth pressures are highest under a given scenario. For example, the Beisanxian County (refer to Figure 2) stands out as facing high pressures for housing development and employment growth under the Trend and Greenbelt scenarios. Such findings could prompt planners to further investigate the potential of transport investment to connect Beijing and Beisanxian, to promote inter-city commuting and relieve overcrowding.

From a local perspective, a greenbelt is unlikely to be the highest performing intervention for Beijing’s economic wellbeing. The negative effects of the greenbelt, such as reduced housing supply (11% lower than Trend), higher housing rent (7% higher than Trend), lower productivity (3% lower than Trend) and a decrease in consumption utility (0.3% lower than Trend), would weaken the leading role of Beijing in the city region. Although Beijing’s greenbelts have been extensively studied in terms of environmental benefits (Gan, 2012; Han and Long, 2010; Tan, 2008; Yang and Zhou, 2007), the findings here indicate that their economic impacts should be studied in more depth. In future strategic greenspace design, the economic as well as environmental effects should be considered. Also, it might be difficult to achieve both environmental and economic benefits through a single greenbelt policy. The possibility of combining greenbelts with other policies, for example congestion tolls (Anas and Rhee, 2006), could be considered and tested using tools like this model.

The green-grid, which stands for a flexible and mixed greenspace configuration, would appear to be an option that better balances economic efficiency and environmental benefits in terms of reducing car usage. Building around metro/rail stations at a relatively high density benefits local residents, as consumption utility sees an overall increase. Compared with Trend, journeys on public transport increase by 28% and active travel by 57%, while those using cars are down by a third at the urban fringe. Such findings are in line with previous studies (Cao and Chatman, 2016; Cervero, 2015; Ewing and Cervero, 2010, 2017). However, our model also finds that housing and business floorspace rents increase in TOD nodes. This indicates that the design of TOD nodes needs to account for such effects and might need to combine other policies to secure floorspace supply.

As the land use demand changes rapidly in Beijing, it appears to be beneficial for policy makers to periodically review where the rules should be relaxed in the designated greenbelt, and to allow patches of land to be developed in certain areas. Such sites need to be carefully selected around metro stations to accommodate the forthcoming growth. More generally, a flexible design of greenspace configurations and a careful siting of new development in the designated greenbelt area could bring potential benefits to residents’ wellbeing.

This model provides a systematic analytical platform for predicting, comparing and analysing the performance of alternative greenspace configurations over time. Under rapid urban transformation, large scale greenspace policies need to be reviewed constantly using tools such as those presented in this research. On the one hand, urban greenspaces need to be implemented as firm designations in order to enhance the quality of the environment. On the other hand, under fast growing conditions, the population sizes and land use needs may radically change from assumptions previously made. It can be beneficial to modify the existing greenspace configurations in line with the new contexts of urban development. This is a difficult call for decision makers, and the new model will help to shed light on the potential economic costs and benefits between the alternative propositions.

This article presents a strategic study examining the economic impacts of greenspaces. The model works on aggregated zones, which makes measuring the sub-zonal level proximity to greenspaces difficult. Also, there is no explicit agency of government or developer, so it is difficult to accurately test the impacts of greenspaces in combination with other policies related to such agencies. Moreover, the model does not develop a platform for comparing the economic costs with the environmental benefits of greenspaces. Instead, it intends to emphasise the importance of assessing economic impacts in the decision making process. In future, the spatial granularity of the model needs be improved and more agencies could be added when data are available to improve the model’s realism. The model could interface with micro-simulation models to improve the representation of consumer and producer behaviour. The model can also interface with other models that are specialised in quantifying environmental impacts to generate a more complete picture to assist decision making.

Supplemental Material

USJ770115_Supplementary_material – Supplemental material for Economic impacts of alternative greenspace configurations in fast growing cities: The case of Greater Beijing

Supplemental material, USJ770115_Supplementary_material for Economic impacts of alternative greenspace configurations in fast growing cities: The case of Greater Beijing by Mingfei Ma, Ying Jin in Urban Studies

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by Cambridge Overseas Trust and the Chinese Scholarship Council; special fund of Key Laboratory of Eco Planning & Green Building, Ministry of Education (Tsinghua University), China; Capco Future Cities Fellowship, Cambridge Real Estate Research Centre; The Cambridge-UC Berkeley-National University of Singapore University Alliance project ’Smart Design’.

Notes

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