人-自然深度共生：风景园林生态规划的整合理论框架探索

Human-Nature Deep Symbiosis: Exploration of the Integrated Theoretical Framework for LandscapeEcological Planning

关键词:  风景园林  风景园林生态规划  深度共生  整合理论框架  学科反思  人-自然耦合  生态感知

Key words:  landscape architecture  landscape ecological planning  deep symbiosis  integrated theoretical framework  discipline reflection  human-nature coupling  ecological approach to perception

摘要:

面对人类世的复杂社会-生态挑战，风景园林生态规划在取得丰富成果的同时，也日益面临如何系统性整合多元研究路径以回应人居环境综合治理 需求的挑战。基于此，构建了“人-自然深度共生”(DEEP-Symbiosis)整合理论框架。该框架借鉴深生态学倡导的互利共生愿景，由“深度嵌套”(DEEPNesting) 的系统认知论、“深度融合”(DEEP-Fitting)的设计过程论与“深度计算”(DEEP-Computing)的技术方法论三大支柱构成。该框架提炼了当前风景 园林生态规划从格局到过程、从静态到动态、从物本到人本，这一不断从“表象”向“深度”演化的趋势，并为5种典型研究范式提供了系统性的理论指引。框架 的提出旨在为风景园林生态规划提供一种系统性的理论指导与方法路径，推动人居环境的可持续发展。

Abstract:

In the Anthropocene, landscape architecture is under pressure to evolve from a practice focused on spatial aesthetics to one of systemic governance for complex socio-ecological systems. While landscape ecological planning (LEP), a core practice area, has produced abundant research in biodiversity, ecosystem services, and spatial networks, it faces a critical challenge. Current approaches, despite their individual strengths, struggle to systematically integrate diverse research streams when addressing complex, multi-objective challenges in human habitat governance. This paper identifies this as a core challenge - a lack of systemic integration - which leads to two persistent practical dilemmas: 1) a failure to effectively translate quantitative scientific analysis into qualitative design language and spatial schema, and 2) a weak capacity to model and integrate the dynamic interactions between objective biophysical processes and subjective human perception. To address these challenges, this article constructs an integrated theoretical framework named "Human-Nature Deep-Symbiosis". The framework is philosophically grounded in the ethical vision of Deep Ecology, advocating for a shift from parasitic or commensal relationships toward "mutualistic symbiosis" between humans and nature. This comprehensive theory is systematically constructed upon three core pillars, each designed to resolve one of the identified theoretical needs: 1) a systemic cognition theory ("DEEP-Nesting"), 2) a design process theory ("DEEP-Fitting"), and 3) a technical methodology ("DEEP-Computing"). The first pillar, "DEEP-Nesting", serves as the framework's systemic cognition theory, addressing the challenge of fragmentation. It draws from Socio-Ecological System (SES) theory to reframe the landscape not as a static container, but as a complex adaptive system of multi-scalar, nested, and coupled human-nature interactions. The second pillar, "DEEP-Fitting", provides the design process theory to bridge the science-practice gap. It builds upon the traditions of Ian McHarg's Design with Nature and John T. Lyle's Design for Human Ecosystems, offering a methodology for adaptively "fitting" human activities within site-specific ecological and cultural processes. The third pillar, "DEEP-Computing", delivers the technical methodology to overcome the subject-object divide. By leveraging computational science and Artificial Intelligence (AI), this pillar provides tools to model, simulate, and quantify the dynamic feedback loops between objective environmental data and subjective human perceptual data. This integrated framework is not merely abstract; it directly drives and operationalizes five typical research paradigms: 1) Multi-Scenario Simulation, 2) Mechanistic Study of Multiple Scenarios, 3) Causal Relationship Modeling, 4) Correlational Relationship Modeling, and 5) Interactive Experiment. These paradigms demonstrate how the three pillars work in synergy, providing concrete pathways to resolve the identified practical dilemmas. In conclusion, the "Human-Nature Deep- Symbiosis" framework offers a systematic, end-to-end logic - from cognition to process to tools - designed to guide landscape architecture research and practice. It provides a robust theoretical and methodological pathway to move the discipline beyond fragmented approaches and toward a truly integrated, systemic, and sustainable future for human-nature co-evolution.