Simulating the Impact of Economic and Environmental Strategies on Future Urban Growth Scenarios in Ningbo, China

Abstract

Coastal cities in China are challenged by multiple growth paths and strategies related to demands in the housing market, economic growth and eco-system protection. This paper examines the effects of conflicting strategies between economic growth and environmental protection on future urban scenarios in Ningbo, China, through logistic-regression-based cellular automata (termed LogCA) modeling. The LogCA model is calibrated based on the observed urban patterns in 1990 and 2015, and applied to simulate four future scenarios in 2040, including (a) the Norm-scenario, a baseline scenario that maintains the 1990-2015 growth rate; (b) the GDP-scenario, a GDP-oriented growth scenario emphasizing the development in city centers and along economic corridors; (c) the Slow-scenario, a slow-growth scenario considering the potential downward trend of the housing market in China; and (d) the Eco-scenario, a slow-growth scenario emphasizing natural conservation and ecosystem protections. The CA parameters of the Norm-and Slow-scenarios are the same as the calibrated parameters, while the parameters of proximities to economic corridors and natural scenery sites were increased by a factor of 3 for the GDP-and Eco-scenarios, respectively. The Norm-and GDP-scenarios predicted 1950 km 2 of new growth for the next 25 years, the Slow-scenario predicted 650 km 2 , and the Eco-scenario predicted less growth than the Slow-scenario. The locations where the newly built-up area will emerge are significantly different under the four scenarios and the Slow-and Eco-scenarios are preferable to achieve long-term sustainability. The scenarios are not only helpful for exploring sustainable urban development options in China, but also serve as a reference for adjusting the urban planning and land policies.

Elderly’s Travel Patterns and Trends: The Empirical Analysis of Beijing

“…The different levels of economic development and different household structures in different regions often cause very different travel behavior [26,27]. In addition, most of the above studies are only a descriptive analysis and lack an inferential analysis on the sample data.…”

Long-Term Regional Environmental Risk Assessment and Future Scenario Projection at Ningbo, China Coupling the Impact of Sea Level Rise

“…Increasing human demand for natural and land resources has put great pressure on the natural environment [44] from facility construction, landscape alteration, urban development and land reclamation [45,46]. This has led to global land-use change, reduction in vegetation coverage, and growth in built-up areas [47].…”

Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm

“…Indeed, it has been shown that CA models, especially GIS-based CA models, can efficiently simulate urban growth processes [16,34,35,36] and estimate spatial patterns of urban growth [36]. However, calibrating an urban CA is challenging and complicated because of the existing spatial heterogeneity of urban forms and the nonlinearity of urban growth driving forces (cf.…”

“…[37,38]). To calibrate urban CA models, some researchers (e.g., [21,30,35,39]) have used statistical approaches, such as multi-criteria evaluation (MCE) or logistic regression (LR), to estimate growth parameters, with moderate success. Fuzzy logic approaches have also been employed to estimate CA parameters; some have even used fuzzy logic to quantify uncertainties in CA models [3,40,41].…”