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研究生：	阮文彥 Juan, Wen-Yen
論文名稱：	公告應回收廢棄物回收處理產業火災風險分析-以高雄市廢塑膠容器回收處理業為例 Research on Fire Risk Analysis for the Recycling and Processing Industry: A Case Study of Waste Plastic Container Recycling and Processing Plants in Kaohsiung City
指導教授：	陳偉聖 Chen, Wei-Sheng
學位類別：	博士 Doctor
系所名稱：	工學院 - 資源工程學系 Department of Resources Engineering
論文出版年：	2025
畢業學年度：	114
語文別：	英文
論文頁數：	302
中文關鍵詞：	廢塑膠容器回收處理業、火災風險、廣義線性模型、蒙地卡羅模擬、系統動力學、尾端風險
外文關鍵詞：	Waste Plastic Container Recycling Industry, Fire Risk, Generalized Linear Model, Monte Carlo Simulation, System Dynamics, Tail Risk
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本研究以高雄市 2017 年至 2024 年間廢塑膠容器回收處理設施之火災資料為基礎，旨在建立一套可量化、可模擬且具政策應用性的火災風險分析架構。目前現行資源回收處理設施的火災研究多停留於靜態統計層面，缺乏對制度、行為與時序交互影響的探討；因此，本論文創新性地整合「廣義線性模型（Generalized Linear Model, GLM）」、「蒙地卡羅模擬（Monte Carlo Simulation, MCS）」與「系統思考與系統動力學（System Dynamics, SD）」三種方法，建立一套跨層次、跨時間尺度的多方法整合分析架構。
首先，GLM分析燃燒面積與搶救時間兩項核心變數，成功捕捉火災資料的右偏與異質特性。模型結果呈現一「反直覺」情形：「到場時間」呈現顯著的「負相關」，即統計上「較長的到場時間」對應到「較小的燃燒面積與滅火時間」。本研究將此歸因為觀測資料中的「統計假象」，受「郊區效應」影響顯著：由於回收場多位於郊區，消防據點分佈較廣，到場時間本就較長；因此，「較短到場時間」往往對應於通報明確的重大火災」，而「較長到場時間」則多為「案情明確的輕微火災」。此發現突顯了單純靜態模型進行因果推論的局限性。此外，「登記狀態」具負向關係但統計不顯著，暗示制度防護的潛在效果。
接著，以 MCS 將 GLM 的估計參數轉化為隨機分佈，進行十萬次抽樣以模擬年度損失分佈與尾端風險。模擬結果確認塑膠回收場火災呈「高頻小損、低頻巨損」的長尾特性，且夜間情境為最顯著的高風險來源。敏感度分析顯示，整體風險具高度不確定性；結果指出改單純增加人力或縮短到場時間的邊際效益則趨於飽和。最後，SD 模型將因果迴路圖轉化為可量化的動態系統，模擬政策介入對長期風險的影響。模擬結果指出，制度性政策對長期穩定性貢獻最大，透過平衡性回饋迴路逐步降低風險基線；操作性政策則能在短期內提升效率，但其效益最終會被系統回饋機制所吸收。綜合二類政策方案可同時強化結構性防護與操作韌性，形成雙重穩定化迴路，展現動態平衡之系統行為。
本研究成功將統計推論（GLM）、不確定性量化（MCS）與動態回饋模擬（SD）串聯。研究結果揭示：火災損害受制度性防護與操作性應變的交互作用支配，長尾風險需以系統性與預防性策略應對。此研究不僅補足國內在廢棄物回收火災風險量化與政策模擬上的缺口，亦為地方政府與消防單位建立以數據為本的防災治理決策模型，具理論創新與實務應用價值。

This study uses fire incident data from waste plastic container recycling facilities in Kaohsiung City from 2017 to 2024 to develop a quantifiable, simulable, and policy-relevant fire risk analysis framework. The research shows that current studies on waste and recycling facility fires mostly remain at a static statistical level, lacking examination of how institutions, behaviors, and time series interact. Therefore, this dissertation innovatively combines three methods: the Generalized Linear Model (GLM), Monte Carlo Simulation (MCS), and System Dynamics (SD), creating a multi-level, cross-temporal integrated analytical framework.
First, the GLM uses a Gamma-log structure to analyze two core variables: burned area and suppression time, effectively capturing the right-skewed and heterogeneous nature of the fire data. The model results reveal a key "counter-intuitive" finding: "arrival time" shows a significant "negative correlation," meaning that, statistically, "longer arrival times" are associated with "smaller burned areas and shorter suppression times." This study attributes this to a "statistical artifact" in observational data, heavily influenced by the "suburban effect": since most recycling sites are located in suburban areas where fire stations are widely spread out, standard arrival times tend to be longer. Consequently, "extremely short arrival times" often correspond to "major fires with high-priority dispatch," while "longer arrival times" are more likely "minor fires with lower priority or delayed reporting." This highlights the limitations of relying solely on static models for causal inference. Additionally, "registration status" shows a negative relationship but is not statistically significant, implying a potential protective effect of institutional safeguards.
Next, MCS converts the GLM-estimated parameters into random distributions, running 100,000 iterations to simulate annual loss distributions and tail risks (VaR, CVaR). The simulation results confirm the long-tail characteristic of "high frequency, small loss; low frequency, catastrophic loss" in fires at plastic recycling facilities, with the nighttime scenario being the most significant source of high risk. Sensitivity analysis reveals overall high uncertainty; results suggest that the marginal benefits of merely increasing manpower or shortening arrival times tend to diminish. Finally, the SD model translates the Causal Loop Diagram (CLD) into a quantifiable dynamic system to simulate how policy interventions impact long-term risk. Results indicate that institutional policies contribute most to long-term stability, gradually lowering the risk baseline through balancing feedback loops; operational policies can improve efficiency in the short term, but system feedback mechanisms ultimately absorb their benefits. Combining both policy types can simultaneously enhance structural protection and operational resilience, creating a dual stabilization loop and demonstrating a system behavior of dynamic equilibrium.
This study uses an integrated multi-method framework, linking statistical inference (GLM), uncertainty quantification (MCS), and dynamic feedback modeling (SD) to reconstruct the multi-level structure of fire risk in the plastic recycling industry in Kaohsiung. The results show that fire damage is influenced by the interaction between institutional protection and operational response, and long-tail risks need to be managed with systemic and preventive strategies. This research not only addresses a gap in domestic quantification and policy simulation of waste-recycling fire risks but also develops a data-driven disaster-governance decision-making model for local governments and fire departments, with theoretical innovation and practical application value.

CHAPTER 1  INTRODUCTION	1
1-1 BACKGROUND	1
1-2 RESEARCH MOTIVATION	4
1-3 RESEARCH OBJECTIVES	7
1-4 SCOPE AND LIMITATIONS	8
CHAPTER 2	LITERATURE REVIEW	12
2-1 INTERCONNECTIONS BETWEEN RECYCLING AND FIRE RISK	12
2-1-1 Taiwan’s Waste Recycling System	12
2-1-2 Characteristics and recycling process of waste plastic containers	14
2-1-3 Fire Risks in Recycling and Processing Facilities	16
2-1-4 Research on Fires in Waste Recycling and Processing Facilities	19
2-2 FIRE CONTROL POLICIES IN THE RECYCLING INDUSTRY: DOMESTIC AND INTERNATIONAL PERSPECTIVES	29
2-2-1 Domestic Control Policies	29
2-2-2 Summary of International Regulations and Technical Guidelines	34
2-2-3 Comparative Synthesis of Domestic and International Policy Frameworks	39
2-3 FIRE RISK ASSESSMENT	42
2-3-1 Fire Risk Assessment Methods	42
2-3-2 Applications of Generalized Linear Models (GLMs) in Fire Risk Analysis	48
2-3-3 Applications of Monte Carlo Simulation (MCS) in Managing Fire Risk Uncertainty	54
2-4 SYSTEM THINKING AND DISASTER MANAGEMENT	62
2-4-1 Theoretical Foundations and Core Concepts of System Dynamics	62
2-4-2 Applications of System Dynamics and System Thinking in Fire Risk and Disaster Management	64
2-5 SUMMARY	68
CHAPTER 3	METHODOLOGY	70
3-1 RESEARCH PROCESS	70
3-1-1 Data collection and pre-processing	70
3-1-2 Variable Definition and Preliminary Analysis	73
3-1-3 Generalized Linear Model	77
3-1-4 Monte Carlo Simulation Design	79
3-1-5 System Dynamics Modeling and Scenario Simulation	82
3-1-6 Integration of Results and Methodological Framework	85
3-2 SAMPLE SELECTION AND DATA DESCRIPTION	89
3-2-1 Background and Scope of the Study	89
3-2-2 Data Scope and Time Horizon	92
3-2-3 Data Collection, Filtering, and Transformation	94
3-2-4 Limitations and Data Suitability	97
3-3 FIRE INCIDENT DATABASE AND KEY VARIABLES	98
3-3-1 Fire Incident Data Sources and Descriptive Statistics	98
3-3-2 Dependent Variable Definition	100
3-4 GENERALIZED LINEAR MODEL (GLM)	102
3-4-1 Theoretical Foundation and Research Applicability	102
3-4-2 Model Design and Mathematical Structure	104
3-4-3 Model Estimation and Goodness-of-Fit Assessment	110
3-4-4 Principles for Variable Selection and Model Design	114
3-4-5 Model Estimation Workflow and Software Environment	116
3-5 DESIGN OF THE MONTE CARLO SIMULATION	120
3-5-1 Simulation Purpose	120
3-5-2 Simulation Framework and Procedure	122
3-5-3 Sampling Method and Simulation Settings	124
3-5-4 Stopping Criteria and Quality Control	127
3-5-5 Loss Proxy Construction	130
3-5-6 Simulation Outputs and Policy Application	133
3-6 SYSTEM THINKING MODEL DEVELOPMENT	139
3-6-1 Framework and Theoretical Basis	139
3-6-2 Causal Loop Diagram Construction	141
3-6-3 System Dynamics Model Transformation	147
3-6-4 Policy Scenario Design	152
3-6-5 Model Validation and Limitations	156
3-7 INTEGRATION OF METHODS AND POLICY SIMULATION FRAMEWORK	160
CHAPTER 4	RESULTS AND DISCUSSION	165
4-1 DESCRIPTIVE STATISTICAL ANALYSIS OF FIRE DATA	165
4-2 GENERALIZED LINEAR MODELS RESULTS AND ANALYSIS	184
4-2-1 Model Fitness Assessment for Dependent Variables	185
4-2-2 Estimation Results of Explanatory Variables	201
4-2-3 Model Comparison and Interpretation	209
4-3 MONTE CARLO SIMULATION RESULTS AND ANALYSIS	213
4-3-1 Annual Fire Loss Distribution and Confidence Interval	213
4-3-2 Risk Sensitivity Analysis	218
4-3-3 Spatial Visualization and Hotspot Analysis of Fire Risk	228
4-3-4 Short-term Forecast and Scenario Analysis of Burned Area	231
4-3-5 Integrative Discussion	239
4-4 SYSTEM DYNAMICS SIMULATION RESULTS AND ANALYSIS	242
4-4-1 Simulation Overview	242
4-4-2 Overview of Simulation Results	245
4-4-3 Feedback Structure and Behavioral Interpretation	253
4-5 INTEGRATED DISCUSSION AND COMPARATIVE ANALYSIS	256
CHAPTER 5	CONCLUSIONS AND RECOMMENDATIONS	261
5-1 RESEARCH OVERVIEW	261
5-2 MAJOR FINDING	263
5-3 THEORETICAL AND PRACTICAL CONTRIBUTIONS	267
5-4 POLICY RECOMMENDATIONS	269
5-5 LIMITATIONS AND FUTURE DIRECTIONS	272
REFERENCES	277
APPENDIX A	284
APPENDIX B	286
                                    

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