Form follows policy – Tracing the effects of sustainable land policies on urbanform

社会辩论和研究越来越多地质疑现行的土地政策。随着城市发展的生态后果变得明显，可持续城市化的目标要求我们在规划和管理城市土地的方式上进行根本性的改变。虽然最近出台了以可持续发展为导向的土地政策，但人们对其带来必要变化的潜力知之甚少。这种知识的缺乏表明需要更好地了解土地政策对城市和区域发展的影响。因此，需要追踪政策对实体城市形态的因果效应，需要分析长期稳定的土地政策的变化。针对这一需要，提出了一种创新的方法，将数据驱动的空间分析与土地政策实践的新机构观点相结合。它涉及对城市发展中复杂的有形城市结构和复杂的制度安排之间的因果关系进行实证分析的方法论挑战。采用数据驱动的方法，在建筑空间布局的层面上分析城市形态。建筑数据的高度细节使人们能够观察土地政策的影响，例如土地使用规划对私人财产使用的规定。进一步的数据被用来分析区位效应和随时间的变化。数据驱动的方法，如机器学习，被应用于城市区域的广泛空间结构和识别城市发展的模式。为了深入了解形式和政策之间的因果关系，将这些空间模式与各自的土地政策实践进行了对比。为此，采用了严格的研究设计，将定量空间分析的解释和因果关系的互补方法与定性案例研究方法相结合。根据观察到的形式和政策之间的相关性，在子案例中仔细审查了行为者在应用土地政策方面的作用。发展项目的案例被用来推断城市发展中的制度逻辑，这些逻辑解释了为什么适用具体的政策。通过比较不同时间点的子案例，找出阻碍或促进可持续土地政策应用的发展路径。为了开发和测试这一方法，需要一个在效果(形式)和原因(政策)上有明显差异的比较环境。这项研究应用于德法边境地区过去30年的住宅开发。尽管德国和法国遵循类似的可持续发展议程，但它们代表着截然不同的土地政策体系。通过追踪可持续土地政策的效果，拟议项目力求通过解决城市发展中的因果推论和概括，在方法论方面推进对土地政策的研究。其目的是为土地利用规划和土地管理的政策制定和评估增加知识生成。

Societal debates and research are increasingly questioning current land policies. As ecological consequences of urban growth become apparent, the goal of sustainable urbanisation demands for fundamental changes in how we plan and manage urban land. While sustainability oriented land policies have been introduced recently, little is known about their potential to bring the required changes. This lack of knowledge points to the need to better understand effects of land policies on the development of cities and regions. Therefore, causal effects of policies on physical urban form need to be traced and changes of long-term and stability oriented land policies need to be analysed. Addressing this need, an innovative approach is proposed that combines data-driven spatial analysis and a neo-institutional perspective on practices of land policy. It addresses methodological challenges for an empirical analysis of causal relations within complex physical urban structures and complex institutional arrangements in urban development. Using data-driven approaches, urban form is analysed on the level of spatial arrangements of buildings. The high level of detail of building data allows to observe the effects of land policies, such as regulations from land use planning for the use of private property. Further data is used to analyse locational effects and change over time. Data-driven approaches, such as machine learning, are applied to extensive spatial structures of city regions and to identify patterns of urban development. To gain insights on causal relations between form and policy, these spatial patterns are contrasted with respective practices of land policy. To do so, a strict research design is applied that combines complementary approaches to explanation and causation from quantitative spatial analysis and qualitative case study approaches. Based on observed correlations between form and policy, the role of actors in applying land policies is scrutinised in sub-cases. Cases of development projects are used to infer on institutional logics in urban development that explain, why specific policies were applied. Through comparing sub-cases at different points in time, development paths that hamper or enable the application of sustainable land policies are identified. To develop and test the approach, a comparative setting is required with clear variation in effect (form) and cause (policy). The research is applied to residential development of the last 30 years in a German-French cross-border area. While Germany and France follow a similar sustainability agenda, they stand for highly different systems of land policies. Tracing the effect of sustainable land policies, the proposed project seeks to advance research on land policy in methodological terms through addressing causal inference and generalisation in urban development. It therewith aims to enhance knowledge generation for policy development and assessment in land use planning and land management.