Research on Carbon Reduction Pathways: A Case Study of Kau Yi Chau Artificial Islands

Abstract

Developing artificial islands is considered to be an effective solution for land scarcity and economic growth in coastal regions. However, the construction and operation of artificial islands could generate significant carbon emissions, posing challenges for low-carbon transitions. In this study, Kau Yi Chau Artificial Islands are analyzed to explore low-carbon strategies tailored to artificial island development. A carbon emission accounting framework based on urban operational processes is established, and five scenarios are developed using the LEAP model: the Baseline Scenario (BAS), Low-Demand Scenario (S1), Regular Carbon Reduction Scenario (S2), Enhanced Carbon Reduction Scenario (S3), and Deepened Carbon Reduction Scenario (S4). Energy demand and carbon emissions are systematically assessed across sectors such as buildings, transportation, solid waste, and vegetation-based carbon sinks. The results indicate that, compared to the BAS, carbon emissions in the S1, S2, S3, and S4 scenarios are reduced by 19.5%, 20.8%, 41.9%, and 54.6%, respectively. S4 is identified as the optimal development pathway for the artificial islands. The carbon reduction contributions of different sectors are analyzed, and optimization measures are proposed, providing valuable insights for low-carbon planning in artificial islands development.

1. Introduction

With rapid economic development and urban expansion, many coastal regions worldwide have transformed into dense metropolitan hubs [1]. This rapid urbanization, coupled with rising population and resource demands, has intensified land scarcity [2]. In response, artificial island and land reclamation projects have emerged globally as strategic solutions to expand urban space. However, the construction and operation of such projects pose significant challenges in terms of carbon emissions, necessitating systematic planning to ensure that these new urban areas transition towards sustainable and low-carbon development models.

As one of the most densely populated regions in the world, Hong Kong has long struggled with land scarcity [3]. With a total land area of just 1114 square kilometers but home to over 7.5 million people, the city has an average population density exceeding 6900 people per square kilometer, making land availability a persistent challenge [4]. To address this challenge, the Hong Kong government proposed the development of the Kau Yi Chau Artificial Islands (KYCAI) as a flagship land reclamation project [5]. This initiative aims to implement smart, green, and low-carbon urban development strategies, serving as a demonstration of sustainable growth. However, its success depends on a comprehensive understanding of potential carbon emissions and the effectiveness of mitigation measures.

Regional carbon emissions have been widely studied across multiple sectors and spatial scales. Increasing attention is being given to how integrated energy transitions contribute to low-carbon urban development. Methodologically, bottom-up carbon accounting and scenario-based simulations are commonly used. The Long-range Energy Alternatives Planning (LEAP) model is particularly prominent [6], having been applied at national [7], provincial [8,9], municipal [10,11], and industrial park levels [12] to support regional decarbonization planning.

For carbon mitigation, existing studies focus on three main approaches. First, market-based tools such as carbon taxes [13,14], emissions trading systems [15,16], and renewable energy subsidies [17] are widely discussed. Second, technological options like CCUS [18,19], hydrogen energy [20,21], energy storage [22,23], bioenergy [24], and smart grids [25] are considered important for improving energy efficiency. Third, structural adjustments are suggested to reduce carbon intensity and support economic growth through industrial upgrading [26,27] and the expansion of the service sector [28,29].

While substantial research has addressed urban carbon emissions, low-carbon spatial planning, and emission reduction strategies in traditional urban contexts, studies focusing on artificial islands remain sparse. Most existing research related to artificial islands has concentrated on construction-stage emissions or the impacts on marine ecosystems [30,31]. Operational emissions have not been adequately addressed. The planning of infrastructure, energy systems, and transportation on artificial islands differs significantly from that of traditional urban areas. Emission interactions across sectors such as buildings, transportation, and residential activity remain insufficiently examined. Few studies have developed integrated models to simulate multi-sector emissions under different spatial planning strategies.

Given the research demand mentioned above, a carbon reduction pathway assessment is conducted for artificial islands under varying spatial development strategies. The KYCAI in Hong Kong are selected as the case study. Sectoral greenhouse gas emissions from buildings, transportation, solid waste, and vegetation-based carbon sinks are quantified during the operational phase. By comparing the emission reduction effects of each scenario, this study identifies the optimal pathway and sector-specific contributions. The findings are expected to provide a scientific basis for low-carbon planning in artificial island development.

2. Research Methodology

2.1. Model Framework

The KYCAI in Hong Kong is investigated through a bottom-up analysis of sectoral energy demand, based on 2023 activity levels. Multiple policy-driven scenarios are developed under carbon reduction constraints, integrating energy efficiency upgrades, structural adjustments in energy systems, and carbon sinks enhancement measures. Post-completion projections of energy consumption and emissions are assessed to identify optimized pathways for achieving carbon neutrality targets.

To establish the carbon emission accounting model framework, the accounting boundaries are first defined according to the specific characteristics of the study area. The KYCAI lacks traditional industrial systems and agricultural activities due to constraints from land reclamation and ecological conservation policies. Therefore, this study focuses on emissions arising from core urban operations, rather than full production-based accounting.

In alignment with IPCC (2019) guidelines and adapted to local urban conditions, emissions are classified into three operational sectors: building operations, transportation, and municipal solid waste management. The building sector includes residential and commercial building operations, primarily involving electricity, heating, and cooling demand. The transport sector comprises private vehicles, public transit, and logistics. The waste sector covers solid waste disposal, landfill, and recycling-related emissions. Additionally, vegetation-based carbon sinks are included to reflect the ecological character of the artificial islands. These are accounted for following urban ecology principles and integrated into the overall carbon balance. The framework is illustrated in Figure 1.

2.2. LEAP Model

The LEAP model, developed by the Stockholm Environment Institute, is a bottom-up modeling tool [32]. It employs scenario analysis to simulate energy demand and environmental impacts under different scenarios by adjusting parameter settings [33]. The carbon emission accounting framework using LEAP aggregates sectoral activity data with energy intensity to estimate energy demand. This estimated energy demand is subsequently used to calculate the total carbon emissions [34]. Due to its low initial data requirements and streamlined model structure, the LEAP model is widely applied in international studies on energy use and carbon emission assessments [35]. Its extensive carbon emission factor database and flexible modeling capabilities further enhance its applicability. Leveraging these advantages, the LEAP-KYCAI model is developed in this study to predict the future carbon emissions of the artificial islands.

(1)

Carbon Emission Calculation for the Building Sector

Carbon emissions from the building sector are typically categorized into two sectors: residential and commercial buildings. CO₂ emissions from residential buildings are generally calculated based on household counts, as follows:

$$C{O}_{2, RB}=\sum _{i=1}^n{\displaystyle \frac{POP}{SI}}\times HE{C}_i\times E{F}_i$$

(1)

where $C{O}_{2, RB}$ represents the CO₂ emissions from residential buildings; $POP$ denotes the population of the artificial islands; $SI$ is the average household size; $HE{C}_i$ is the average household energy consumption for energy type $i$, and $E{F}_i$ is the carbon emission factor for energy type $i$.

Similarly, the CO₂ emissions from commercial buildings are calculated based on building area using the following formula:

$$C{O}_{2, CB}=\sum _{i=1}^n{A}_{CB}\times PE{C}_i\times E{F}_i$$

(2)

where $C{O}_{2, CB}$ represents the CO₂ emissions from commercial buildings; $A_{CB}$ is the total commercial building area; $PE{C}_i$ is the energy consumption per unit area for energy type $i$; and $E{F}_i$ is the carbon emission factor for energy type $i$.

(2)

Carbon Emission Calculation for the Transportation Sector

The calculation of CO₂ emissions from the transportation sector is calculated by incorporating transport activity levels and fuel efficiency parameters. The emissions are quantified using the following formula:

$$C{O}_{2, T}=\sum _{i=1}^n{S}_i\times L_i\times F_i\times E{F}_i$$

(3)

where $C{O}_{2, T}$ represents the CO₂ emissions from the transportation sector; $S_i$ denotes the number of vehicles; $L_i$ is the average mileage per vehicle; $F_i$ represents the fuel efficiency level of the vehicle; and $\mathrm{E}{F}_i$ is the carbon emission factor for fuel type $i$.

(3)

Carbon Emission Calculation for Solid Waste

CO₂ emissions from waste disposal are quantified based on the total waste generation volume. In Hong Kong, landfilling dominates waste management, with greenhouse gas emissions primarily originating from methane (CH₄). These emissions are converted to CO₂-equivalent values using a conversion factor. The calculation is formulated as follows:

$$C{O}_{2, W}=PCW\times \left(1-r\right)\times Coe\times \phi$$

(4)

where $C{O}_{2, W}$ represents the CO₂ emissions from waste disposal; $PCW$ denotes per capita waste generation; $r$ is the waste recycling rate; $Coe$ is the carbon emission factor for waste treatment; and $\phi$ is the methane-to-CO₂ conversion coefficient.

(4)

Carbon Sink Calculation

The estimation of carbon sink is primarily based on the vegetation coverage area. In urban areas of Hong Kong, the main types of vegetation include trees, shrubs, and grasslands. The carbon sink capacity is quantified using the following formula:

$$C{O}_{2, H}=\sum _{k=1}^n{A}_k\times Q_k$$

(5)

where $C{O}_{2, H}$ represents the carbon sink capacity; $A_k$ denotes the area of vegetation type $k$; $Q_k$ corresponds to the carbon sink coefficient for vegetation type $k$.

2.3. Data Sources

This study predicts and analyzes the carbon emissions of the KYCAI upon completion, based on Hong Kong’s 2023 energy efficiency benchmarks. The research utilizes primary data from authoritative sources, including the Hong Kong Annual Digest of Statistics [36], Hong Kong Energy End-use Data [37], and the Guidelines for Greenhouse Gas Emission Accounting and Removals from Buildings in Hong Kong [38]. The fossil fuel emission factors applied in the model are sourced from the IPCC default values embedded in the LEAP framework. These data sources form the basis of the Baseline Scenario (BAS), which reflects the expected carbon performance if current technologies and behaviors continue without further intervention.

2.4. Scenario Design

In addition to the Baseline Scenario (BAS), four scenarios are proposed: the Low-Demand Scenario (S1), the Regular Carbon Reduction Scenario (S2), the Enhanced Carbon Reduction Scenario (S3), and the Deepened Carbon Reduction Scenario (S4). These scenarios evaluate potential emission outcomes under varying levels of policy ambition and technological adoption. Parameter settings for key sectors such as buildings, transportation, solid waste, and vegetation are guided by the strategic targets in the Hong Kong Clean Air Plan 2035 [39] and the Hong Kong Climate Action Plan 2050 [40]. The scenarios are constructed by translating official targets into concrete modeling parameters, considering different assumptions about policy enforcement, technological deployment, and behavioral transformation. The four scenarios represent progressively ambitious carbon reduction pathways, ranging from moderate mitigation with limited behavioral changes to rapid decarbonization driven by strong policies, advanced technologies, and widespread demand-side transformation. Scenario descriptions, parameter configurations, and implementation measures are systematically outlined in

Table 1. Scenario description and key parameter settings.

Scenario	Accounting Sector	Scenario Description
BAS (Baseline Scenario)	Building	Maintain the current average energy consumption level for buildings in Hong Kong.
	Transportation	The proportion of NEVs in the motor vehicle fleet is 8.6%.
	Solid Waste	per capita solid waste generation is fixed at 1.51 kg/person/day
	Vegetation	The greenery coverage rate is set at 20%.
S1 (Low-Demand Scenario)	Building	Construct 20% ultra-low-energy buildings and 20% nearly zero-energy buildings.
	Transportation	The proportion of NEVs in the motor vehicle fleet is 20%.
	Solid Waste	Remains consistent with BAS.
	Vegetation
S2 (Regular Carbon Reduction Scenario)	Building	Remains consistent with S1.
	Transportation
	Solid Waste	Per capita waste generation is reduced by 15%, with a recycling rate reaching 40%.
	Vegetation	Greenery coverage is increased to 25%, with MPTS occupying 55% of the green area, and 10% rooftop greening.
S3 (Enhanced Carbon Reduction Scenario)	Building	Construct 40% ultra-low-energy buildings and 40% nearly zero-energy buildings.
	Transportation	The proportion of NEVs in the motor vehicle fleet is 40%.
	Solid Waste	Per capita waste generation is reduced by 30%, with a recycling rate reaching 50%.
	Vegetation	Greenery coverage is increased to 30%, with MPTS occupying 70% of the green area, and 20% rooftop greening.
S4 (Deepened Carbon Reduction Scenario)	Building	Construct 50% ultra-low-energy buildings and 50% nearly zero-energy buildings.
	Transportation	The proportion of NEVs in the motor vehicle fleet is 60%.
	Solid Waste	Per capita waste generation is reduced by 45%, with a recycling rate reaching 55%.
	Vegetation	Greenery coverage is increased to 35%, with MPTS occupying 85% of the green area, and 30% rooftop greening.

.

(1)

Baseline Scenario (BAS)

In this scenario, the energy consumption benchmarks for residential and commercial buildings in the study area are standardized to align with Hong Kong’s 2023 energy efficiency standards. The share of new energy vehicles (NEVs) in the total vehicle fleet is set to match the current NEVs registration rate in Hong Kong, which is approximately 8.6%. The greenery coverage within the study area is assumed to be the same as that of urban Hong Kong: around 20%. Furthermore, per capita solid waste generation is fixed at 1.51 kg/person/day, consistent with Hong Kong’s official waste management statistics from 2023.

(2)

Low-Demand Scenario (S1)

Building on the BAS scenario, priority is given to transforming the building and transportation sectors. The widespread adoption of ultra-low-energy buildings and near-zero-energy buildings is advocated. Ultra-low-energy buildings are regarded as a preliminary stage of near-zero-energy buildings. They maintain equivalent indoor environmental parameters but exhibit slightly lower energy efficiency. Typically, they achieve over a 50% reduction in energy consumption compared to national and industry benchmarks. Near-zero-energy buildings achieve up to a 75% reduction in energy consumption. Meanwhile, under this scenario, the deployment of charging infrastructure is accelerated to substantially increase the adoption rate of NEVs.

(3)

Regular Carbon Reduction Scenario (S2)

Based on the S1 scenario, a solid waste charging scheme is implemented according to the “polluter pays” principle. Fees are levied according to the weight of waste produced. This approach incentivizes waste reduction and recycling by individuals and industries. As a result, per capita solid waste disposal is effectively reduced. Additionally, greenery coverage on the artificial islands is enhanced by adopting mixed planting of trees and shrubs (MPTS). This method combines trees and shrubs into a complementary vertical structure. It optimizes light and land use while improving ecological functions and carbon sink capacity. Rooftop greening is also promoted to further enhance the urban environment.

(4)

Enhanced Carbon Reduction Scenario (S3)

Building on the S2 scenario, this scenario accelerates the adoption of ultra-low energy and near-zero-energy buildings. While promoting NEV adoption through expanded charging infrastructure, the scenario aims to increase market penetration. Additionally, stricter waste disposal and recycling regulations are implemented to improve compliance rates. The scenario also strengthens the islands’ carbon sink capacity through large-scale afforestation initiatives.

(5)

Deepened Carbon Reduction Scenario (S4)

As the most comprehensive scenario, S4 strengthens carbon reduction efforts across all sectors, including buildings, transportation, and waste management. It significantly enhances greenery coverage throughout the artificial islands, increases the proportion of mixed planting of trees and shrubs (MPTS) within green areas, and optimizes rooftop designs to widely implement green roofs.

3. Results

3.1. Analysis of Energy Demand

The projected energy demand for the KYCAI across multiple scenarios is presented in Figure 2. As energy-saving and carbon reduction measures are progressively implemented, the forecasted energy demand exhibits a downward trend. In the BAS, where no additional green/carbon reduction measures are introduced, the energy demand is estimated at 1574.1 kilotons. In the S1 scenario, the introduction of ultra-low-energy and near-zero-energy buildings, as well as the promotion of electric vehicles, results in a decrease in energy demand. The most significant reduction is observed in electricity consumption. The S2 scenario incorporates waste reduction, recycling enhancement, and carbon sequestration initiatives. However, these non-energy-sector measures yield minimal impact on energy demand levels, resulting in outcomes comparable to those of the S1 scenario. In the S3 scenario, additional energy-saving and carbon reduction measures lead to a reduction in energy demand to 1093.6 kilotons. This represents a 30.5% decrease compared to the BAS. In the S4 scenario, a comprehensive decarbonization strategy leads to a further reduction in energy demand, lowering it to 765.1 kilotons. This represents a 42.8% decrease compared to the BAS, highlighting the significant impact of deep decarbonization measures.

The sectoral energy demand under different scenarios is shown in Figure 3. Commercial buildings consistently account for the largest share of total energy consumption, highlighting a dominant role in the energy landscape. Specifically, the proportion of energy attributed to commercial buildings in the BAS, S1, S2, S3, and S4 scenarios is 78.1%, 77.1%, 77.1%, 76.4%, and 73.9%, respectively. This trend demonstrates that the increasing adoption of ultra-low-energy and near-zero-energy buildings in the planning of the KYCAI directly correlates with a gradual decline in commercial building energy demand. Consequently, the proportion of total energy consumption attributed to commercial buildings decreases slightly, underscoring the critical role of high-efficiency building systems and technologies in optimizing energy use patterns. Such improvements not only reduce overall energy consumption but also contribute to a more balanced and sustainable energy demand structure.

The sectoral energy demand under different scenarios is presented in Figure 4. In the BAS, transportation energy demand is dominated by petroleum and coal products, accounting for 82.7% of total sectoral consumption. However, as EV adoption progresses on the KYCAI, the dependence on fossil fuels demonstrates a steady decline, reflecting the transition towards low-carbon-mobility solutions. In the S4 scenario, this proportion drops significantly to 53.3%, underscoring the transformative impact of EV adoption on energy structural optimization. Concurrently, buildings exhibit a pronounced dependence on electricity. In residential buildings, electricity accounts for approximately 71.1% of total energy consumption, while in commercial buildings, it represents as much as 89.0%. This result is consistent with Ortiz-Peña et al. [41], indicating that electricity consumption dominates the energy usage profile during the building operational phase. This dominance highlights electricity’s central role in building energy systems, necessitating further integration of renewable energy sources to decarbonize the grid.

3.2. Analysis of Carbon Source

The carbon emissions under different scenarios are presented in Figure 5, showing a consistent downward trend in line with the reduction in energy demand. As energy-saving and carbon reduction measures are progressively implemented, total carbon emissions decrease from 3725.3 kilotons in the BAS to 1691.8 kilotons in the S4 scenario. While these integrated actions achieve significant emission reductions, they fall short of carbon neutrality, underscoring the necessity for enhanced cross-sector coordination and additional technological or policy innovations to fully realize net-zero emissions.

As illustrated in Figure 6, electricity consumption remains the dominant source of emissions, accounting for 85.2% of total carbon emissions in the S4 scenario. This highlights the critical role of the power sector in achieving carbon reduction. This result aligns with Zhang and Luo [42], indicating that both independent measures on the energy consumption side and coordinated actions on the supply side are essential for achieving the zero-carbon target. To mitigate emissions from electricity generation, prioritizing grid decarbonization via renewable energy integration and carbon capture and storage becomes imperative.

3.3. Analysis of Carbon Sink

The carbon sink levels under different scenarios are shown in Figure 7. The results indicate that MPTS exhibits a significant advantage in terms of carbon sink, with a carbon sink capacity several times higher than that of grasslands. This result echoes the findings of Warner et al. [42], which also demonstrated that mixed planting is superior to monocultures in carbon storage. This study further validates the conclusion in the context of regional carbon reduction pathways. This necessitates prioritizing mixed plantings in urban greening designs to maximize carbon sink potential. Such strategies not only enhance urban ecological resilience but also mitigate the urban heat island effect, thereby improving residents’ quality of life. The contribution of rooftop greening to urban carbon sink remains relatively limited, primarily due to two constraints. First, building load capacities and construction limitations restrict rooftop greening to low-growing, shallow-rooted plants, which exhibit significantly lower carbon sequestration efficiency per unit area compared to deep-rooted vegetation. Second, rooftop greening areas remain substantially smaller than traditional carbon sink systems, such as ground-level parks and wetland reserves, due to spatial limitations. Consequently, the overall carbon sequestration effectiveness of rooftop greening is constrained. This result contrasts with the conclusions drawn by Nadeeshani et al. [43], who reported that green roofs have significant carbon reduction potential over the entire building lifecycle. This discrepancy may be due to this study considering only the carbon sequestration effect of green roofs through plant photosynthesis, without accounting for the role in reducing heat transfer and subsequently lowering building energy demand.

3.4. Analysis of Emission Reduction Effects in Key Sectors

The masterplan specifies that upon full completion, the KYCAI is expected to accommodate approximately 500,000 permanent residents and generate an annual economic output of approximately HKD 200 billion [44]. With respect to environmental sustainability,

Table 2. Emissions per capita and intensity of carbon emissions under different scenarios.

Scenario	BAS	S1	S2	S3	S4
Per capita carbon emissions (ton)	7.45	5.99	5.89	4.32	3.38
Carbon emissions intensity (kgCO2e per HKD GDP)	0.018	0.015	0.015	0.011	0.008

consolidates and presents both the anticipated per capita carbon emissions and the corresponding carbon emission intensity under different scenarios. To assess the islands’ carbon emissions, this study compares these indicators with the carbon intensity constraints and per capita carbon emission targets set by the Hong Kong Climate Action Plan 2030+ [45]. The results indicate that under the S4 scenario, the islands’ carbon emissions meet the baseline requirements of the policy targets. Therefore, the S4 scenario is selected as a case study to further examine the contributions of various emission reduction measures in lowering carbon emissions.

As illustrated in Figure 8, carbon mitigation measures under the S4 scenario exhibit varying degrees of effectiveness on the artificial islands. The building sector is identified as the predominant emission contributor, thereby achieving the most significant mitigation impact. A relatively low contribution rate is observed in the transportation sector, driven by two structural constraints. First, vehicle ownership is naturally restricted by the KYCAI’s geographical limitations. Second, the decarbonization of the current power grid remains incomplete, thereby diminishing the projected emission reduction advantages of electric vehicle adoption. Hong Kong’s waste management system remains predominantly dependent on landfilling practices. Within the BAS, waste decomposition emissions on the artificial islands account for merely 3.3% of total emissions, indicating minimal mitigation potential due to this low baseline. Regarding urban vegetation planning, vegetation planning priorities must be strategically balanced between biodiversity preservation and carbon sequestration efficiency under green space constraints, rather than prioritizing high-sequestration species exclusively. Consequently, even with optimized carbon sink strategies, sequestration capacity remains subject to multi-dimensional operational limitations.

4. Discussion

4.1. Low-Carbon Development Pathways

Achieving low-carbon development on artificial islands requires coordinated efforts in energy efficiency, clean transportation, waste management, urban greening, local renewable energy, and marine carbon sinks.

(1)

Optimizing Energy Efficiency in Commercial Buildings.

Mitigation efforts in the building sector should prioritize commercial buildings, where energy consumption primarily relies on grid electricity, with minimal direct utilization of coal or petroleum-based fuels. Building design optimization, particularly through passive energy-saving technologies, enables significant operational energy reduction without compromising indoor environmental comfort [46]. These strategies include the application of high-performance thermal insulation materials in building envelopes, the implementation of rationally designed daylighting and ventilation systems, and the integration of rooftop photovoltaic systems [47,48]. When combined with flexible energy management mechanisms such as demand-response systems, these measures enhance operational energy efficiency, thereby establishing a technical foundation for the near-zero carbon transition in the building sector [49].

(2)

Advancing Low-Carbon Transition in Transportation Systems.

Enhancing the adoption rate of NEVs should be prioritized as a core strategy for establishing sustainable transportation systems. In the private vehicle sector, the transition to electric vehicles must be accelerated through purchase subsidies and usage-based incentive policies to promote fleet renewal [50]. For public service applications, hydrogen fuel cell vehicles are recommended as the primary solution to address high-frequency, long-distance operational requirements [51]. Charging and hydrogen refueling infrastructure must be expanded in parallel, enhancing accessibility to ensure proportional growth in clean vehicle utilization. This integrated approach will facilitate the transition toward a transportation framework dominated by clean energy vehicles.

(3)

Implementing Integrated Waste Management Solutions.

A waste management system integrating source reduction, classified recycling, and waste-to-energy conversion should be established [52]. Waste generation per capita can be effectively curbed through the implementation of a pay-as-you-throw pricing mechanism, while smart sorting facilities are recommended to enhance material recovery rates. Meanwhile, advanced treatment facilities should be constructed to minimize landfill dependency, thereby reducing carbon emissions from waste processing [53,54]. This comprehensive strategy facilitates the transition of the artificial island’s solid waste management toward a circular economy framework.

(4)

Developing Eco-Friendly Greening Systems.

Urban greening planning should be strategically designed to balance ecological effectiveness and implementation feasibility. MPTS should be prioritized to enhance carbon sink efficiency per unit area. In high-density construction zones, three-dimensional greening approaches such as rooftop vegetation systems must be implemented to expand green coverage, thereby improving the artificial island’s overall greening ratio [55].

(5)

Enhancing Local Renewable Supply and Marine Carbon Sinks

Achieving zero-carbon development requires not only demand-side reductions but also systematic transformation of the energy supply system. Despite progress under the S4 scenario, electricity demand on the islands is primarily met through externally supplied power. To enable local decarbonized supply, abundant marine, solar, and wind resources should be strategically harnessed through large-scale deployment of offshore wind turbines, rooftop photovoltaic systems, and tidal energy technologies [56]. In parallel, the marine ecological assets surrounding the artificial islands present significant potential for carbon sequestration. Marine ecological protection and biodiversity conservation should be prioritized in island planning. Additionally, mariculture systems could be integrated to enhance CO₂ absorption through seawater and marine organisms [57], contributing to a blue carbon strategy that supplements traditional carbon sinks and supports the island’s carbon neutrality targets.

4.2. Policy and Cost Considerations

The S4 scenario achieves the highest emission reductions by integrating advanced technologies and comprehensive strategies, but it also requires substantial upfront investments, especially in building ultra-low-energy buildings and expanding NEV infrastructure. Constructing ultra-low-energy buildings significantly contributes to emission reductions, but also entails higher capital costs. According to Li et al. [58], each additional level of green building certification increases construction costs by approximately SGD 25.37 per square meter. These increased expenditures may pose challenges for project developers, particularly in large-scale projects like the Kau Yi Chau Artificial Islands. Similarly, expanding NEV charging infrastructure requires considerable investment in equipment, grid capacity, and long-term maintenance, which may further strain project budgets [59,60]. In comparison, improving solid waste management and increasing vegetation-based carbon sinks incur relatively low costs but, as shown in Figure 8, have limited emission reduction effects. This suggests that while basic improvements in low-cost sectors should not be neglected, the bulk of investment should be directed toward high-impact interventions. Existing policy frameworks like Hong Kong’s Climate Action Plan 2050 offer a solid foundation for this approach, but clearer cost–benefit guidelines and stronger cross-sectoral coordination are needed to guide implementation effectively.

4.3. Contribution to the Field

Compared to the majority of existing carbon mitigation studies that primarily focus on conventional terrestrial urban environments [61,62], this study specifically addresses the distinctive land use planning, energy supply challenges, and marine ecological contexts inherent to artificial islands. Unlike previous investigations that concentrate on single sectors such as building energy consumption or transportation emissions [63,64], this study integrates a comprehensive suite of mitigation strategies encompassing buildings, transportation, solid waste management, and urban greening, thereby providing a more holistic framework for carbon emission assessment.

In terms of methodology, this study develops a LEAP model tailored to the energy supply and demand characteristics of artificial islands, enabling detailed scenario analysis of energy consumption and carbon emissions. Unlike previous studies that commonly used Computable General Equilibrium (CGE) models to assess economy-wide carbon reduction impacts [65,66], this study adopts a scenario-based LEAP model tailored to the specific energy demand and supply conditions of artificial islands. While CGE models are useful for analyzing macroeconomic effects, they often lack spatial and sectoral resolution, making them less suitable for capturing the localized energy structure and spatial planning characteristics of artificial islands. In contrast, this study systematically integrates strategies across multiple domains such as buildings, transportation, and solid waste into a replicable and scalable framework. This comprehensive approach offers practical insights for future offshore urban developments and large-scale reclamation projects.

4.4. Limitations and Future Research Directions

This study has several limitations that require further refinement. First, the emission estimations are based on a set of scenario assumptions, such as the future energy mix, transportation mode shifts, and building performance levels. While these assumptions are informed by policy targets and best practices, they remain subject to future policy changes, technological uncertainties, and behavioral variations, which may influence actual carbon outcomes. Second, because the artificial island project is still in the planning stage, much of the data in areas such as building energy consumption, transportation demand, and waste generation was estimated using reference values from comparable cities and regional averages. These assumptions may lead to discrepancies when compared to the actual conditions that will emerge with regard to the KYCAI in the future. Third, the study focuses on operational-phase emissions and does not fully quantify embodied carbon in construction materials and infrastructure, which may constitute a significant portion of life cycle emissions. Although these emissions were qualitatively discussed, a full life cycle assessment (LCA) was beyond the scope of this research.

Future studies could address these limitations by incorporating dynamic scenario models that reflect a wider range of policy and behavioral trajectories, utilizing real-time data once the project progresses, and conducting full-scale LCA to capture embodied emissions. Integrating stakeholder feedback and high-resolution simulation tools would also help enhance the accuracy and robustness of carbon assessments for artificial islands in the long term.

5. Conclusions

Artificial islands present an essential solution to the challenge of coastal land scarcity and must adhere to low-carbon development principles in line with carbon neutrality goals. This study proposed a comprehensive carbon emission accounting framework and utilized the LEAP model to simulate future energy consumption and emissions during the operation phase. The results indicate that commercial buildings are the primary drivers of energy demand, with electricity being the dominant energy source. This underscores the critical role of expanding renewable energy and deploying carbon capture, utilization, and storage (CCUS) technologies. Among all sectors, the building sector contributes the most to emission reductions, emphasizing the importance of improving energy efficiency and increasing the share of clean energy within buildings. While transportation, waste management, and vegetation carbon sinks account for a smaller share of the overall emission reduction potential, they remain integral to the ecological and environmental sustainability of artificial islands. Overall, this research establishes a methodological framework for carbon emission forecasting and mitigation specific to artificial islands, with a focus on urban operational processes. It contributes to the theoretical advancement of regional carbon emission studies and offers practical guidance for low-carbon development in coastal megaprojects.