Report on Environmental Impact Analysis of Building Demolition in Alignment with Sustainable Development Goals

Executive Summary

This report details an analysis of the environmental impacts of building demolition, a critical phase in the construction lifecycle with significant implications for achieving the Sustainable Development Goals (SDGs). The construction sector’s contribution to climate change and environmental degradation necessitates a shift towards more sustainable practices. This study integrates System Dynamics (SD) modeling with the Random Forest (RF) machine learning algorithm to assess the environmental performance of four different demolition tool combinations (OEIC1, OEIC2, OEIC3, and OEIC4). The findings provide a robust framework for aligning demolition activities with SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). The analysis confirms that the combination of a demolition robot and hydraulic splitter (OEIC4) is the most environmentally sustainable option, producing impacts up to four times lower than conventional methods. The RF model validated the SD model with high predictive accuracy (R-squared values of 0.9776 to 0.9990), underscoring the reliability of this integrated approach for informing policy and promoting a circular economy, reducing pollution, and supporting global climate mitigation efforts.

1.0 Introduction: Aligning Demolition with Sustainable Development Goals

The construction industry is a major driver of economic growth but also a significant contributor to environmental challenges, directly impacting the progress of several SDGs. Building demolition, a major source of waste and pollution, presents a critical challenge to sustainable urban development. This report examines demolition practices, specifically for townhouses in Bangkok, Thailand, through the lens of sustainability.

The environmental consequences of demolition—including energy consumption, greenhouse gas emissions, noise, dust, and vibration—are direct obstacles to achieving key global targets:

• SDG 11 (Sustainable Cities and Communities): Urban growth necessitates demolition, but conventional methods degrade the urban environment through pollution and waste, undermining the goal of creating safe, resilient, and sustainable cities.
• SDG 12 (Responsible Consumption and Production): Demolition generates vast quantities of Construction and Demolition Waste (CDW), and inefficient practices lead to resource depletion and landfill overuse, contradicting the principles of a circular economy and responsible production patterns.
• SDG 13 (Climate Action): The use of fossil-fuel-powered equipment in demolition results in significant CO₂ emissions, directly contributing to climate change.

This study addresses these challenges by developing an integrated analytical framework to guide the strategic selection of demolition tools, thereby mitigating environmental harm and advancing a more sustainable construction sector.

2.0 Methodological Framework for Sustainable Impact Assessment

To comprehensively analyze the complex interactions within the demolition process, this study adopts an integrated methodology combining System Dynamics (SD) modeling and the Random Forest (RF) algorithm. This approach supports SDG 9 (Industry, Innovation, and Infrastructure) by applying innovative analytical techniques to foster sustainable industrial practices.

2.1 System Dynamics (SD) and Random Forest (RF) Integration

The methodology was designed to capture the dynamic and non-linear relationships between demolition activities and their environmental consequences.

1. System Dynamics (SD) Modeling: An SD model was developed to simulate the long-term environmental impacts of different demolition scenarios over a 30-year period. The model incorporated key variables such as the number of townhouses, demolition duration, tool work rates, and six primary environmental impacts.
2. Random Forest (RF) Algorithm: The RF algorithm, a supervised machine learning technique, was used to validate the accuracy of the SD model’s outputs. It assessed the predictive performance of the model and identified the most influential variables (feature importance), enhancing the reliability of the findings for decision-making.

2.2 Assessed Demolition Scenarios and Environmental Impacts

The analysis focused on four combinations of demolition tools commonly used or available for townhouse demolition:

• OEIC1: Excavator, jackhammer, and flame cutter
• OEIC2: Excavator, jackhammer, and hydraulic splitter
• OEIC3: Excavator and hydraulic splitter
• OEIC4: Demolition robot and hydraulic splitter (representing a modern, sustainable technology approach)

These combinations were evaluated against six critical environmental impacts, weighted by their significance as identified in the scientific literature:

• CO₂ Equivalent Emissions (Weight: 20.8)
• Primary Energy Consumption (Weight: 6.3)
• Noise (Weight: 2.3)
• Dust (Weight: 2.0)
• Heat (Weight: 1.8)
• Vibration (Weight: 1.0)

3.0 Analysis of Findings and Contribution to SDGs

The results provide clear, actionable insights for promoting sustainable demolition practices that align with multiple SDGs.

3.1 Superior Environmental Performance of Advanced Technology

The analysis conclusively identifies OEIC4 (demolition robot and hydraulic splitter) as the most sustainable option. It generated an overall environmental impact nearly four times lower than the other combinations. This finding directly supports:

• SDG 13 (Climate Action): Demolition robots, often electrically powered, drastically reduce CO₂ emissions and primary energy consumption compared to traditional diesel-powered excavators.
• SDG 11 (Sustainable Cities and Communities): Robotic systems operate with lower noise and dust levels, minimizing disruption and health risks in dense urban environments.
• SDG 12 (Responsible Consumption and Production): The precision of robotic demolition enables selective deconstruction, which improves material separation and increases the potential for recycling and reuse, thereby fostering a circular economy.

3.2 Impact of Demolition Duration and Model Validation

The sensitivity analysis confirmed that shorter demolition durations significantly reduce the overall environmental impact. This reinforces the need for efficient operational planning to achieve sustainability goals.

The RF algorithm validated the SD model with exceptional accuracy, confirming the reliability of the study’s conclusions:

• R-squared (R²) values: Ranged from 0.9776 to 0.9990, indicating the model explains over 97% of the variance in the data.
• Mean Squared Error (MSE) scores: Were extremely low (0.0003 to 0.0356), signifying high precision in predictions.

This robust validation provides a credible, data-driven basis for formulating environmental policies aimed at the construction sector.

4.0 Policy Implications and Recommendations for Sustainable Demolition

The findings of this report offer a clear pathway for policymakers, engineers, and contractors to transition towards sustainable demolition practices.

4.1 Promoting Technological Innovation and Sustainable Infrastructure

To advance SDG 9 and SDG 11, policies should be developed to encourage the adoption of low-impact technologies. Recommendations include:

• Implementing financial incentives, such as subsidies or tax credits, for companies that invest in demolition robots and other green technologies.
• Updating public procurement criteria to prioritize demolition contractors who use sustainable methods and technologies.
• Establishing industry-wide training programs to build a skilled workforce capable of operating advanced demolition equipment.

4.2 Strengthening Climate Action and Circular Economy Frameworks

To support SDG 13 and SDG 12, the construction industry must integrate sustainable demolition into broader environmental strategies:

1. Set Carbon Reduction Targets: Use the analytical framework from this study to establish specific emission reduction targets for the demolition phase of construction projects.
2. Integrate into Circular Economy Policies: Mandate selective demolition and waste sorting on-site to maximize the recovery of recyclable materials, reducing landfill dependency and promoting resource efficiency.
3. Optimize Timelines: Encourage project planning that minimizes demolition duration, as this has been proven to lower the overall environmental footprint.

5.0 Conclusion

This report demonstrates that the integration of System Dynamics modeling and the Random Forest algorithm provides a powerful and reliable framework for assessing and mitigating the environmental impacts of building demolition. The findings unequivocally show that the adoption of advanced technologies, particularly demolition robots (OEIC4), is critical for aligning the construction industry with the Sustainable Development Goals.

By generating significantly lower emissions, consuming less energy, and reducing local pollution, sustainable demolition practices contribute directly to climate action (SDG 13), the creation of sustainable cities (SDG 11), responsible consumption and production (SDG 12), and industry innovation (SDG 9). The validated model presented in this study offers a data-driven foundation for stakeholders to formulate effective policies, optimize demolition strategies, and drive the transition to a more sustainable and circular construction sector.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

1. SDG 9: Industry, Innovation, and Infrastructure

   • The article directly connects to this goal by focusing on the construction industry, a key component of infrastructure. It explores innovations like “demolition robots,” “System Dynamics (SD) modeling,” and the “Random Forest (RF) algorithm” to create more sustainable and efficient industrial processes (demolition). The study’s aim to provide an “integrated framework” and a “novel method” for assessing environmental impacts aligns with fostering innovation.

2. SDG 11: Sustainable Cities and Communities

   • The research is set within the context of urban development in the “Bangkok Metropolitan Region (BMR).” It addresses the challenges of managing urban growth, housing demolition, and the resulting environmental impacts on cities. The article specifically discusses reducing adverse environmental effects like “air and noise pollution,” “dust,” and “vibrations” that impact the quality of life for urban residents, which is a core concern of SDG 11.

3. SDG 12: Responsible Consumption and Production

   • This goal is central to the article’s discussion on “Construction and demolition waste (CDW),” which is identified as a “major global environmental concern.” The study’s focus on reducing waste, promoting “material recycling,” and contributing to “circular economy principles” directly addresses the need for sustainable management and efficient use of natural resources and the reduction of waste generation.

4. SDG 13: Climate Action

   • The article explicitly states that the construction sector has a “substantial contribution to climate change” and that its proposed approach aligns with “global climate change mitigation goals.” It quantifies key climate-related impacts, including “CO2 equivalent emissions,” “greenhouse gas (GHG) emissions,” and “primary energy consumption,” directly linking the demolition process to climate action.

2. What specific targets under those SDGs can be identified based on the article’s content?

1. Target 9.4: Upgrade infrastructure and retrofit industries to make them sustainable

   • The article directly supports this target by evaluating and promoting the adoption of “cleaner and more environmentally sound technologies” in the demolition industry. The analysis of different tool combinations, particularly the finding that “demolition robots and hydraulic splitters” are the most sustainable option, is a clear effort to retrofit an industrial process for increased resource-use efficiency and reduced environmental impact.

2. Target 11.6: Reduce the adverse per capita environmental impact of cities, paying special attention to air quality and waste management

   • The study’s focus on the environmental impacts of demolition in Bangkok directly addresses this target. It analyzes and quantifies “dust and particulate matter” (air quality), “noise,” and “Construction and Demolition Waste (CDW)” (waste management), providing a framework to mitigate these specific urban environmental problems.

3. Target 12.5: Substantially reduce waste generation through prevention, reduction, recycling, and reuse

   • The article highlights that “demolition is a major contributor to global waste generation” and that “concrete waste constituting the largest proportion.” By analyzing methods to minimize environmental impact, which includes waste, and by mentioning that the choice of demolition method impacts “material recycling,” the study directly contributes to the goal of reducing CDW.

4. Target 13.2: Integrate climate change measures into national policies, strategies, and planning

   • The research provides a “validated framework to support policy recommendations for sustainable demolition practices.” By quantifying “CO2-equivalent emissions” and identifying it as the most significant environmental impact, the study offers a data-driven tool for policymakers to integrate climate change mitigation strategies into construction and demolition regulations.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

1. Indicators for Target 9.4

   • Adoption of environmentally sound technologies: The article compares four different combinations of demolition tools (OEIC1-4), with the use of “demolition robots” in OEIC4 representing a modern, sustainable technology. Progress could be measured by the adoption rate of such advanced systems in the industry.

2. Indicators for Target 11.6

   • Generation of solid waste: The article identifies “Construction and demolition waste (CDW)” and “concrete waste” as major issues. The volume of this waste is a direct indicator.
   • Air pollution concentration: The study explicitly measures “respirable dust” in “mg/m³,” which serves as a direct indicator for air quality.
   • Noise pollution levels: The model calculates “noise” levels in “decibels,” providing a clear indicator of this urban pollutant.

3. Indicators for Target 12.5

   • Waste generation rate: The entire study is premised on modeling and reducing the impacts of demolition, which includes the amount of waste produced.
   • Material recycling rate: The article mentions that sustainable practices “support material recycling” and “enhance material recovery,” implying that the percentage of recycled CDW is a key indicator.

4. Indicators for Target 13.2

   • CO2 equivalent emissions: The article explicitly calculates “CO2-eq emissions” in “kg CO2-eq” and identifies it as the most heavily weighted environmental impact. This is a primary indicator for climate action.
   • Primary energy consumption: Measured in “kgoe,” this is a key input for calculating emissions and is an indicator of the fossil fuel dependency of demolition processes.

4. Summary Table of SDGs, Targets, and Indicators

Source: nature.com