A Framework for Assessing the Effectiveness of Carbon Storage Change During the Process of Land Consolidation

Abstract

Land consolidation (LC) plays an important role in disturbing carbon storage (CS) change. Evaluating how LC affects CS is crucial for mitigating global climate change. However, existing research often overlooks differences in various aspects of land remediation, making it challenging to propose targeted policy adjustments to enhance CS effectiveness. This study presents a framework to assess the effectiveness of CS changes throughout the LC process, encompassing policy formulation stages (PF), construction stages (CO), and post-management stages (PM). Carbon density, a key factor in measuring CS changes, is adjusted using biomass model-integrated empirical measurements with dynamic growth coefficients calibrated through phenological monitoring. The Guangdong Demolition and Reclamation (D&R) project, a specific type of LC, serves as a case study. The findings are as follows: (1) D&R increased forest and garden land by 1420 hm² and 1674 hm², respectively, leading to a regional CS increase of 359,000 t, a five-fold rise per hectare. (2) The effectiveness of PF is 5.81%, with a discrepancy of over 36 million tons. The policy content’s adaptability is low, indicating significant room for improvement in CS outcomes at this stage. (3) The effectiveness of CO is 24.71%, with considerable variation between counties, ranging from 1.26% to 97.55%, due to the varying capabilities of executors and the diverse regional topographical features. Refining implementation content and encouraging collaborative efforts are effective strategies to enhance CS. (4) The effectiveness of PM is 65.03%, and the counties in the east are lower than the west. Scientific post-care is essential for improving CS. This framework provides theoretical support for optimizing LC to enhance regional CS and lays the groundwork for future investigations into the long-term impacts of LC on CS, as well as the potential for applying the methods used in this study to other regions and types of land consolidation projects.

1. Introduction

Land-use/cover change has greatly altered terrestrial carbon storage (CS) and is essential for reducing the greenhouse effect and controlling climate change [1]. Global terrestrial CS is estimated to be approximately 2500 billion tons, with forests alone accounting for nearly 80% of this storage [2]. As an essential environmental service, carbon storage plays a crucial role in mitigating climate change [2]. It not only contributes to reducing atmospheric carbon but also helps maintain ecological balance. The capacity of ecosystems, such as forests, gardens, and wetlands, to store carbon varies significantly, with forested areas and garden lands typically offering much higher CS than agricultural or barren lands [3]. Understanding the different roles of various ecosystems and vegetation types in carbon storage is vital for developing effective carbon management strategies. Land consolidation (LC), one of the key tools of land-use change, has been widely used worldwide [4], mainly because LC can increase regional CS by optimizing land structure [5,6]. Although the scale of the LC has increased in rural China recently, the effectiveness of CS production during the LC process remains unclear [7,8]. Most recent studies have focused on assessing carbon storage after LC [5,9], ignoring the variations at different stages of LC. The limited understanding of the CS dynamics in LC processes restricts the formulation of precise policy interventions that could better optimize CS outcomes [10].

LC is an important driver of regional CS changes. The reclamation of land types with high carbon storage potential, such as forests and garden lands, is expected to become increasingly important in LC efforts under the dual carbon goals of reduction and peaking [11,12]. In these LCs, land cover transitions directly affect the vegetation and soil carbon storage, which are the primary components of the region’s CS [7]. Specifically, vegetation carbon storage increases when areas with low or no biomass are converted to high-biomass land types like gardens or forests through LC [10]. Furthermore, soil organic carbon storage is affected by the soil–atmosphere continuum rate due to the improvements in regional land structures and the increasing amount of ecological land [13]. Meanwhile, areas where vegetation carbon storage increased have an increase in soil carbon storage due to the sustained output of plant litter occurring with the increase in vegetation cover [14,15]. Anthropogenic activities can subtly alter regional carbon storage during LC; for instance, fertilizers used in farming decrease soil organic carbon storage [16], while crop harvesting and packaging increase carbon emissions [17]. Shan et al. [18] reported that land cover transitions are a major contributor to CS changes. Therefore, CS changes in LC can be quantified by analyzing the change in vegetation and soil carbon storage caused by land cover transition [7].

However, understanding the ecological principles of carbon stock outputs from LC alone will not improve the effectiveness of CS production. An effective assessment needs to integrate the principles of policy implementation into a comprehensive evaluation framework. In fact, most assessments of LC’s impact on carbon stocks only calculate the overall change in carbon storage, focusing on post-LC variation [19,20,21,22,23,24]. But these results alone do not provide an accurate and complete demonstration of policy implementation effectiveness, hindering the ability to make precise recommendations for policy improvements regarding carbon stock realization [25]. This is because the initiation and transmission of new policies are not linear or straightforward but influenced by multiple interacting factors [26]. This complexity and dynamism in policy dissemination and implementation across different stages and regions necessitate a multi-stage assessment framework [27]. Such a framework should evaluate the local adaptability of policy content, the effectiveness of construction, and the level of post-management [12,14,18]. Existing studies, including those by Yang et al. [12] and others, have emphasized the need for comprehensive frameworks to assess LC policies, particularly focusing on their adaptability and impact across different stages of implementation. The results of these assessments can inform potential policy impacts [28] and determine the extent to which adjustments are needed to enhance land remediation effectiveness in realizing CS [29].

In addition, the primary target for LC under the carbon neutrality objective is fragmented rural land [4]. The accurate measurement of carbon density, including both vegetation and soil carbon densities, is essential for calculating carbon storage [30,31,32]. Numerous studies have examined changes in soil carbon density changes in LC [13,19,20], allowing us to draw upon previous research for these calculations. For instance, Li et al. utilized meta-analysis to evaluate the impact of various farmland management measures, such as fertilization and organic amendments, on soil carbon density in LC areas [20]. However, estimating changes in vegetation carbon density presents challenges, particularly on a small scale. Remote sensing models, which are considered empirical models, rely on statistical relationships and existing observational data to estimate carbon storage. These models are advantageous for quick, large-scale estimates but fail to account for the dynamic nature of vegetation growth and ecological processes [33]. In contrast, climate-vegetation models, which are process-oriented models, estimate carbon storage changes by simulating biogeochemical processes, including climate, vegetation growth, and carbon cycling [34]. While these models can capture carbon dynamics more comprehensively, they require extensive parameters and intricate soil–plant–atmosphere interactions, limiting their applicability at smaller scales. Although remote sensing and climate-vegetation hybrid models work well at regional scales, their spatial resolution of 250–1000 m is insufficient for small-scale (<1 ha) land consolidation interventions [35]. To address this issue, the biomass formula offers a more practical hybrid approach, combining empirical measurements such as diameter at breast height and plant height with dynamic growth coefficients calibrated through phenological monitoring. Additionally, the climatic zone estimation method refines regional divisions based on Köppen’s classification system [36], enabling more localized studies. Unlike static climatic zone estimations, the biomass model integrates time-dependent biomass accumulation curves [37,38] and species-specific allometric adjustments for 27 major Guangdong cultivars [39], allowing for in situ validation through rapid measurement protocols. These data can be easily obtained with simple calculations, demonstrating excellent operability. This approach strikes a balance between theoretical rigor and operational feasibility [39,40], making the biomass formula applicable for widespread use in scattered and replanted vegetation areas.

Therefore, the primary objective of this study is to develop a multi-stage framework for assessing the effectiveness of CS during the LC process. This study specifically focuses on evaluating CS outcomes at various stages, including policy formulation, construction, and post-construction management. The research centers on Guangdong Province’s “Demolition and Reclamation” (D&R) policy, investigating how converting rural construction land into ecological land enhances regional CS and supports carbon neutrality goals. Drawing on the existing literature and empirical data, the following hypotheses are tested: (1) The applicability of LC formulation affects the effectiveness of CS realization in the policy formulation stage. (2) The complexity and emergent nature of land use change projects lead to regional differences in the effectiveness of CS at the construction stage. (3) The post-construction management stage is a crucial stage for sustaining the effectiveness of CS. This study aims to test these hypotheses and provide empirical evidence to inform the refinement and implementation of LC policies. The structure of this paper is as follows: Section 2 outlines the policy background and theoretical framework, describing the relationship between land consolidation and carbon neutrality and introducing the nonlinear transfer mechanisms at play. Section 3 discusses the materials and methods, detailing the study area, data sources, and computational processes. Section 4 presents the results, while Section 5 offers a discussion of the research hypotheses. Finally, Section 6 concludes with this study’s findings and policy recommendations.

2. Policy Background and Theoretical Foundations

2.1. Land Consolidation and Carbon Neutrality

LC is a global phenomenon, aimed at enhancing land productivity, improving land-use efficiency, and addressing social, economic, and environmental challenges in rural development [41]. As an integrated concept [42], LC incorporates land reallocation, readjustment, rearrangement, and reclamation. Originating in Europe and formally implemented in the mid-18th century, LC has become a key component of land policies in many countries, particularly in Western Europe [43,44]. China began implementing LC projects in the late 1990s to ensure food security and maintain the quantity and quality of agricultural land [45]. Although China’s experience with LC is relatively recent, significant research and practical work have been undertaken by Chinese scholars [46]. LC in China has evolved through three phases: compensating for lost arable land, integrating arable land with rural settlements, and restoring land ecological functions [47]. Since the reform and opening-up period, rapid urbanization in China has led to a reduction in arable land, intensifying the pressure on agricultural resources [48]. Since the mid-1990s, China has increased the amount of arable land through LC to compensate for lost arable land [49]. In response, China has increased the area of arable land through LC to offset these losses, with the Dynamic Equilibrium of Total Arable Land policy being a representative practice. This policy mandates that for every unit of arable land converted for construction, an equivalent amount of land in terms of quality and quantity must be created by local government [50]. As the focus of LC shifted to addressing issues like village depopulation and idle rural settlements, China entered a new phase [14]. The Boundless Expanse of Fertile Farmland Construction policy, part of the integration phase, consolidates large contiguous areas of farmland and optimizes rural living by relocating residents to urban centers. In the ecological restoration phase, the D&R involves demolishing structures on unused rural construction land, converting the land into gardens or forests with CS functions, and allowing the converted land to be traded as land-use indicators across regions.

Table 1. Typical practices in various phases of land consolidation.

Phases	Representative Practices	Land–Use Change	Impact in the Practice Area
			Area of Ecological Land	Soil Carbon Storage 1	Plant Carbon Storage 2
Compensating for lost arable land	The Dynamic Equilibrium of Total Arable Land policy	Construction land transfers to arable land	- 3	-	-
Integrating arable land with rural settlements	The Boundless Expanse of Fertile Farmland Construction policy	Rural construction land transfers to arable land	↑ 3	↑	↑
Restoring ecological functions	The Demolition and Reclamation policy	Rural construction land transfers to garden or forest land	↑	↑	↑

¹. Soil carbon storage includes soil fertility and soil organic carbon content. ². Plant carbon storage includes plant maturity cycle and accumulation. ³. - and ↑ represent no and positive effects on the practice area, respectively.

details typical local practices, land-use changes, and their impacts across the three phases.

Table 1 shows that the third phase of LC focuses on land’s ecological benefits, gradually becoming a key channel for regional carbon neutrality. Thus, the object of this study focuses on LC in the third stage. With carbon neutrality targets approaching, all levels of government in China are increasingly prioritizing enhancing carbon sinks and reducing emissions. As a result, the government of Guangdong Province, China, prioritizes reclaiming high-carbon-potential lands such as gardens and forests. The D&R policy, formulated by the Guangdong provincial government, is part of the third phase of LC. This policy aims to reduce inefficient rural construction, increase soil and vegetation carbon storage, and support regional economic development and carbon neutrality [51].

Figure 1 illustrates its implementation process:

• Policy formulation stage (PF): The top–level government develops the policy based on regional needs, setting objectives, operational mechanisms, registration thresholds, and promotional methods. Villagers independently decide whether to participate.
• Construction stage (CO): Local governments create construction plans with the characteristics of registered sites and execute construction projects systematically.
• Post-construction management stage (PM): Following the completion of construction, the top-level government conducts inspections to ensure the project meets acceptance criteria. Periodic verification ensures that reclamation plots maintain their status, including planting quantity and quality.

Thus, the primary policy cycle of this initiative encompasses the policy formulation, construction, and post-construction management stages.

Figure 1. Policy cycle of land consolidation.

Figure 1. Policy cycle of land consolidation.

2.2. Effectiveness Theory of Land Consolidation Policy

The nonlinear transfer mechanism highlights the various interactions and feedback loops that occur from the formulation to the implementation of LC policies, collectively influencing their effectiveness [52]. Nonlinearity suggests that the effectiveness of policies is neither straightforward nor predictable; it depends not only on the design intentions but also on numerous internal and external factors, such as local context, incentive structures, government enforcement, and regional dissemination [27]. This mechanism can result in different outcomes for LC policies across various regions or stages, adding complexity to policy formulation [26]. Therefore, understanding and applying the nonlinear transfer mechanism in evaluating LC policies are crucial for enhancing their effectiveness [53].

As conceptually framed in Figure 2, the nonlinear transmission mechanism of LC policies unfolds through three critical junctures, involving both expected and actual trajectories of CS across different stages. The expected trajectory, represented by the dashed line, assumes a linear progression from PF to construction CO to PM stages, where each stage corresponds to idealized CS targets (desired CS 1-3). This model envisions a smooth and predictable transition between stages, with outcomes directly tied to policy objectives. In contrast, the actual trajectory, depicted as a solid polyline, represents the dynamic interactions between various factors influencing each stage. This results in stage-specific deviations from the expected CS targets (actual CS 1-3). These deviations reflect the complexity of real-world conditions, where multiple factors interact in nonlinear ways, leading to variability in outcomes across stages.

The curvature of the pathway at each transition, from PF to CO and then from CO to PM, signifies the varying effectiveness in carbon storage at each stage. At each stage, different impact factors (impact factor a, b and c) influence the effectiveness of LC policies. These variations in curvature highlight the differing levels of resource consumption and effectiveness at each juncture. This variability in curvature reflects the nonlinear nature of the system, where each phase experiences varying levels of efficiency and effectiveness. Consequently, these differences are essential for understanding the overall dynamics of LC policies and underscore the importance of conducting stage-specific assessments to accurately capture the nuances of the carbon storage process.

2.2.1. Policy Formulation Stage

China’s land consolidation policy design reflects a multi-level governance structure where central guidelines interface with local implementation realities [54]. Within this framework, local governments navigate the dual imperatives of adhering to national land-use quotas while addressing regional socio-economic demands [55]. This institutional configuration, while ensuring policy coherence at the macro level, may engender implementation challenges, including asymmetric information flows between planning and operational tiers, divergent incentive structures across administrative hierarchies or limited feedback mechanisms for grassroots stakeholders. As demonstrated in Chengdu’s CURCL pilot [56], such structural characteristics can constrain local adaptive capacity. The case study revealed how villager collectives developed innovative compensation negotiation protocols to bridge policy-prescribed standards and community expectations, suggesting institutionalized participatory channels could mitigate implementation friction.

The concept of policy adaptability was first discussed in the early 20th century, with Dewey suggesting that policies be treated as experiments to promote continuous learning and adaptation over time [57]. Since then, adaptability has been increasingly recognized as vital for enhancing policy effectiveness. For instance, Bizikova evaluated the adaptability of climate change policies in four Canadian provinces through interviews with provincial government staff using the Adaptive Design and Assessment Policy Tool [58]. Scholars have refined and specified principles for improving policy adaptability [59,60,61], including conducting regular policy reviews, using diverse policy tools to foster innovation, designing automatic adjustment mechanisms for rapid responses, and decentralizing decision making to improve grassroots-level decisions.

In terms of the effectiveness of CS outputs in LC, weak policy adaptability, such as the lack of an automatic adjustment mechanism for rapid responses, resulting in some areas not being replaced in a timely manner even if the soil acceptance criteria are not appropriate, reduces policy attractiveness and hinders implementation, thereby lowering CS yield. Based on this, the following hypothesis is proposed:

Hypothesis 1. 

The applicability of LC formulation affects the effectiveness of CS realization in the PF.

2.2.2. Construction Stage

The effectiveness of land-use change projects is a critical topic in engineering. Construction projects are complex and emergent, influenced by multiple factors [62]. Specifically, a construction project is a composite system comprising interconnected elements, such as tasks, personnel, equipment, materials, and knowledge [63,64]. Zhu and Mostafavi [64] interviewed 19 senior project managers and found that when project team members are involved early in the process and collaborate closely, they are better able to develop strategies to address these complexities. One such strategy is coordinating mechanical, electrical, and plumbing systems during the design phase, which helps to prevent potential conflicts between different trades. Moreover, the overall behavior of a construction project is not simply the sum of its parts but can elicit new, unpredictable characteristics [65]. The complexity is further compounded by the regional context in which the project is implemented. For example, when a county-level government possesses adequate staffing and a rich knowledge base on CS, the implementation of the project is more likely to be efficient, and the project plan is more likely to favor CS production.

Hypothesis 2. 

The complexity and emergent nature of land-use change projects lead to regional differences in the effectiveness of CS at the CO.

2.2.3. Post-Construction Management Stage

PM conducted after the completion of land-use and land cover change projects includes monitoring, resowing, and cutting weeds in LC plots [66]. Upon completing land-use change projects, villagers often build houses on converted land to re-engage in LC or similar projects to obtain extra revenue illegally. This behavior contradicts the purpose of land-use change projects and hinders the normal growth of crops and CS on the plots. This stage aims to supervise and prevent this behavior and consolidate the achievement of CS effects [66]. Based on this, we propose the following hypothesis:

Hypothesis 3. 

PM is a crucial stage for sustaining the effectiveness of CS.

The elements involved in PM can be categorized into management subjects, objects, and scenarios [25]. Compared to the last two phases, the third phase of LC focuses on carbon neutrality goals and undergoes significant transformations [25]. These transformations are evident in multi-departmental coordination and grassroots maintenance, with local governments collaborating extensively with forestry and climate-related institutions. Increasing attention is given to the power of village collectives and villagers at this stage [16]. The comprehensiveness and complexity of management objects have increased, requiring not only the prevention of unauthorized land-type changes by villagers but also the maintenance of crop-planting structures and systems on the plots [67]. Management scenarios are diverse, with post-consolidation ecological and economic scenarios forming the basis for maintenance, while attention to cultural contexts in management areas has also increased.

2.3. Theoretical Framework

Based on the theory of policy nonlinear transmission mechanisms and Research Hypotheses 1-3, this study develops a multi-stage framework to evaluate the effectiveness of CS outputs in LC. This framework unfolds across three stages: PF, CO, and PM. As illustrated in Figure 3, the policy cycle is defined according to the stages outlined in Figure 2 in Section 2.1. The framework also defines four types of areas corresponding to each stage: potential area, registered area, accepted area, and verified area. From these areas, four corresponding CS change values are derived, reflecting the carbon sequestration impact at each stage. The effectiveness of CS at each stage is determined by analyzing the margin and ratio between these adjacent CS change values.

Part 1: Defining the Four Areas. The four areas are defined based on the workflow of LC projects: (1) Potential Area: This area represents the total available rural construction land in the region, excluding the area required to meet the registered standards and regulations for LC projects, which are set by the government in alignment with sustainable development goals in Guangdong. (2) Registered Area: This area is established after villagers complete the necessary registration and approval processes to participate in the LC project, reflecting the willingness of villagers to engage in the project. (3) Accepted Area: This area is generated once an acceptance test is conducted at the completion of the LC project (project plots that meet the acceptance criteria form this area). (4) Verified Area: This area is defined after the government verifies that the land use and planting of the plots align with the results of the acceptance test, ensuring the sustainability of the land-use changes.

Part 2: CS Change Mechanism in LC. LC transforms areas with low or no biomass into land uses with higher biomass, such as gardens or forests. This transformation significantly increases both vegetation and soil carbon storage. Once the area is determined, the corresponding CS change values are established. The four CS change values represent the CS impact resulting from the transformation of the potential, registered, accepted, and verified areas into gardens and forests. These values are calculated by multiplying the area by the respective carbon densities of the land uses.

Part 3: Evaluating the Effectiveness of Carbon Storage. The effectiveness of CS at each stage is assessed by comparing the margin and ratio between the corresponding carbon storage change values: (1) PF: The closer the registered CS value is to the maximum ideal CS, the higher the effectiveness of policy formulation. This indicates a strong willingness among villagers to participate, as more plots meeting the project initiation criteria have been registered. (2) CO: The closer the accepted CS is to the registered CS, the higher the effectiveness during construction. This suggests smooth construction processes, with a higher number of plots meeting acceptance standards on time. (3) PM: The closer the verified CS is to the accepted CS, the higher the effectiveness during post-construction maintenance. This reflects that more plots continue to be classified as reclaimed land according to regulations and that vegetation on these plots is well maintained.

3. Materials and Methods

3.1. Study Area

Guangdong Province is located between 20°09′–25°31′ N and 109°45′–117°20′ E in Southern China. The urbanization rate of Guangdong ranks high in China, but the development of this province exhibits significant disparities in each county [68]. Furthermore, the province has the juxtaposition of an unmet urban construction land demand in the Pearl River Delta (PRD) but a large amount of unused rural construction land in northern, western, and eastern Guangdong [69,70], resulting in a waste of land resources. Thus, increasing the ecological and economic value of rural construction land, especially realizing its carbon storage function, is fundamental to achieving the sustainable development of Guangdong. D&R has disturbed carbon storage considerably, warranting further research. D&R started in 2018, with a trial period of 3 years. Thus, the length of this study was from 2018 to 2021, and the scope covers 86 counties (total counties in Guangdong Province: 122) (Figure 4).

According to the practice of Guangdong, this study partially adjusted the classification for the Pearl River Delta (PRD), northern, eastern and western Guangdong, and, thereby, the division subtly varies from the customary classification.

3.2. Date

All calculation formulas used in this study are shown in Figure 5, including revised calculations for changes in land-use area, vegetation and soil carbon density, and variations in carbon storage resulting from changes in land-use area. The data were divided into three categories according to the carbon storage calculation formula: vegetation and soil carbon densities and land-use-type change area.

Vegetation carbon density: Guangdong Province was divided into four zones, namely, the PRD, the Eastern, Western and Northern Guangdong Province, based on the difference in land-use frequency, rainfall and heat, which impact the plant growth. And, then, 24 plots were randomly selected from four zones to be measured, including forest and garden lands (shown in Figure 1). According to the vegetation carbon density formula [39], we mainly measured the indicators with a leather ruler, including the average ground diameter (cm), diameter at breast height (cm), height of the trees (m), volume of storage (m³/hm²) and density per hectare to modify the vegetation carbon density in the garden and forest lands of each zone (shown in

Table A1. The relative indexes for modifying vegetation carbon density of forest land.

Region	Sample Plot	Land Type	Plant Specie	Height (m)	Diameter at Breast Height (cm)	Plot Area (hm2)	Total Amount	Volume of Single Tree (m3)	Volume of Storage (m3/hm2)	Biomass (t/hm2)	Carbon Coefficient (g/kg)	Vegetation Carbon Density (t/hm2)
Pearl River Delta	Gaoming	Forest land	Handroanthus chrysanthus (Jacq.) S.O.Grose	3.00	7.60	100.47 × 10−2	1005	0.012702077	12.70	13.79	522.19	7.20
	Kaiping	Forest land	Bauhinia × blakeana Dunn	2.20	5.00	160.00 × 10−2	667	0.004031707	1.68	1.88	520.67	0.98
	Xinhui	Forest land	Ficus microcarpa L. f.	2.10	12.30	666.67 × 10−2	1852	0.023289266	6.47	7.10	522.19	3.71
Northern Guangdong	Wenyuan	Forest land	Lagerstroemia speciosa (L.) Pers.	2.00	6.80	478.20 × 10−2	1406	0.006779132	1.99	2.23	520.67	1.16
	Lechang	Forest land	Osmanthus fragrans (Thunb.) Lour.	3.40	11.20	109.73 × 10−2	392	0.031263763	11.17	11.89	522.19	6.21
	Yangshan	Forest land	Acacia auriculiformis A. Cunn. ex Benth	1.50	13.00	113.33 × 10−2	354	0.018582505	5.81	6.51	533.07	3.47
Eastern Guangdong	Chaonan	Forest land	Terminalia neotaliala Capuron	3.00	6.00	266.67 × 10−2	833	0.007916807	2.47	2.70	520.67	1.41
	Jiedong	Forest land	Celtis sinensis Pers.	3.40	9.50	266.67 × 10−2	1111	0.022493261	9.37	10.03	520.67	5.22
	Raoping	Forest land	Bischofia javanica Bl.	2.50	12.00	300.00 × 10−2	1071	0.026389356	9.42	10.23	522.19	5.34
Western Guangdong	Suixi	Forest land	Lagerstroemia speciosa (L.) Pers.	3.00	7.20	300.00 × 10−2	1364	0.011400202	5.18	5.63	520.67	2.93
	Leizhou	Forest land	Delonix regia (Boj.) Raf.	3.00	11.00	300.00 × 10−2	1000	0.026609267	8.87	9.53	522.19	4.98
	Gaozhou	Forest land	Elaeocarpus decipiens Hemsl.	3.00	10.00	306.67 × 10−2	1022	0.02199113	7.33	7.90	522.19	4.12

and

Table A2. The relative indexes for modifying vegetation carbon density of garden land.

Region	Sample Plot	Land Type	Plant Specie	Height (m)	Diameter at Breast Height (cm)	Plot Area (hm2)	Density per Hectare	Biomass (t/hm2)	Carbon Coefficient (g/kg)	Vegetation Carbon Density (t/hm2)
Pearl River Delta	Enping	Garden land	Citrus × limon (Linnaeus) Osbeck	1.60	4.00	0.20 × 10−2	600	0.86	522.19	0.45
	Boluo	Garden land	Clausena lansium (Lour.) Skeels	2.00	7.00	15.93 × 10−2	450	2.29	520.67	1.19
	Huidong	Garden land	Psidium guajava Linn.	0.77	3.00	0.10 × 10−2	825	0.51	522.19	0.26
Northern Guangdong	Xinfeng	Garden land	Eriobotrya japonica (Thunb.) Lindl.	1.80	6.00	2.67 × 10−2	675	2.39	520.67	1.25
	Guangning	Garden land	Amygdalus persica L.	1.80	8.00	0.40 × 10−2	720	4.71	522.19	2.46
	Yingde	Garden land	Camellia-oilfera Abel	1.00	6.00	1.60 × 10−2	1050	3.07	533.07	1.64
Eastern Guangdong	Longhu	Garden land	Lagerstroemia indica L.	1.20	7.00	1.33 × 10−2	525	2.26	520.67	1.18
	Chenghai	Garden land	Lagerstroemia parviflora Roxb.	1.40	7.00	2.47 × 10−2	900	4.08	520.67	2.12
	Luhe	Garden land	Citrus maxima	1.50	6.50	2.62 × 10−2	300	1.19	522.19	0.62
Western Guangdong	Dianbai	Garden land	Musa nana Lour.	5.00	11.20	0.97 × 10−2	1050	19.67	520.67	10.24
	Yangdong	Garden land	Dimocarpus longan Lour.	3.00	12.00	2.45 × 10−2	345	6.33	522.19	3.30
	Yangxi	Garden land	Litchi chinensis Sonn.	2.00	20.00	2.17 × 10−2	270	12.86	522.19	6.71

).

Soil carbon density: Existing literature data from soil carbon density studies in Guangdong and neighboring regions (Table 1) were used to correct the soil carbon density in garden and forest lands of each region. It followed the principle that field measurement data from Guangdong were used as the primary data. If not, the national average or the area adjacent to Guangdong data was used as a reference. We use the soil organic carbon density data from the soil layer depth less than thirty cm because levelling and renovation works had been performed in all D&R plots less than three years ago and the soil was neophyte [71].

Land-use types change area: (1) Land-use data were obtained using Landsat 8 remote sensing data inversion [47], which were used to calculate the land area converted from construction to ecological land in each county of Guangdong from 2018 to 2021. (2) Area converting during PF: Rural population data for each county obtained from the 2021 Guangdong Rural Statistical Yearbook (http://stats.gd.gov.cn/gdnctjnj/content/post_3141073.html, accessed on 1 January 2025) and the area of rural construction land in each county of Guangdong obtained from the 2021 China County Statistical Yearbook (http://www.stats.gov.cn/zs/tjwh/tjkw/tjzl/202302/t20230215_1908004.html, accessed on 1 January 2025) were used to calculate the potential area. (3) Area converting during CO: The 2018–2021 database of Guangdong D&R (data from Guangdong Province Land Development and Consolidation Center), including the location, name, area, reclamation direction, and implementation cycle of D&R projects, which were used to calculate the registered area, accepted area and plant biomass, was used. (4) Area converting during PM: The verification report on D&R (data from Guangdong Province Land Development and Consolidation Center) was compiled for the data after the verification work, which lasted from September 2020 to August 2021, including the verification area, time, pass rate [pass rate is the ratio of the area of plots with non-quality problems in the county to the total area that has been inspected], and verification failure reasons, which were used to calculate the verified area.

Analysis of Implementation Effectiveness Variations: The data used in this section come from two sources: 120 counties from the verification report and 20 counties from field research. The data from the 120 counties, sourced from the verification report, represent a wide range of regions and conditions. These data are used to analyze the overall effectiveness of the policy across different areas. Additionally, the data from the 20 field-researched counties involve specific plot samples, which are used to further refine the analysis. The combined data from these two sources will be used in the Discussion section to explain the reasons behind the varying effects observed across different counties. The data from these counties provide in-depth insights into the factors influencing carbon storage outcomes, helping to analyze the policy’s impact in different regions in more detail.

3.2.1. Estimation of Vegetation Carbon Density in Forest and Garden Lands

The modification of vegetation carbon density for forest and garden lands was calculated using plant biomass and carbon coefficient [72] (Table A1 and Table A2) through the Guangdong single-timber growth model constructed by Xue et al. [39]. The plant, soil, and total carbon densities of the forest and garden lands are listed in Table 2. The formula used is as follows:

$${\mathrm{C}}_{\mathrm{v}\mathrm{e}\mathrm{g}\mathrm{e}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}^{\mathrm{k}}={\mathrm{P}}^{\mathrm{k}}\times {\mathrm{C}}_{\mathrm{c}}^{\mathrm{k}}$$

(1)

$${\mathrm{P}}_1^{\mathrm{k}}=1.23764\times {\mathrm{D}}^{-0.02809}\times {\mathrm{H}}^{-0.067526}\times \mathrm{V}$$

(2)

$${\mathrm{P}}_2^{\mathrm{k}}=0.0000645384\times {\mathrm{d}}^{2.12837}\times {\mathrm{H}}^{0.32853}\times \mathrm{N}$$

(3)

In Equations (1)–(3), ${\mathrm{C}}_{\mathrm{v}\mathrm{e}\mathrm{g}\mathrm{e}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}^{\mathrm{k}}, {\mathrm{P}}_1^{\mathrm{k}}, {\mathrm{P}}_2^{\mathrm{k}}, {\mathrm{C}}^{\mathrm{k}}$ are the vegetation density, plant biomass 1 and 2, and carbon coefficients, respectively, after D&R implementation. We use two equations to calculate the plant biomass according to the type of plants, specifically, (2) for the calculation of plant biomass in forest land planted with tall trees and (3) for small trees or shrubs in garden land [39,73]. d, D, H, V and N represent the average ground diameter (cm), diameter at breast height (cm), height of the trees (m), volume of storage (m³/hm²) and density per hectare, respectively.

Table 2. Carbon density and sources table of forest and garden lands in each area (t/hm²).

Area	Land-Use Type	Soil Organic Carbon Density	Sources
Pearl River Delta	Forest land	3.33	[74,75]
	Garden land	2.97	[76]
Northern Guangdong	Forest land	5.52	[74,77]
	Garden land	5.37	[77]
Eastern Guangdong	Forest land	4.64	[74]
	Garden land	4.64	[76]
Western Guangdong	Forest land	5.09	[74,78]
	Garden land	5.34	[78]

Table 2. Carbon density and sources table of forest and garden lands in each area (t/hm²).

Area	Land-Use Type	Soil Organic Carbon Density	Sources
Pearl River Delta	Forest land	3.33	[74,75]
	Garden land	2.97	[76]
Northern Guangdong	Forest land	5.52	[74,77]
	Garden land	5.37	[77]
Eastern Guangdong	Forest land	4.64	[74]
	Garden land	4.64	[76]
Western Guangdong	Forest land	5.09	[74,78]
	Garden land	5.34	[78]

3.2.2. Calculation of Carbon Storage

The formula of calculation of CS is chosen from the Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) model. CS is the product of the total area and carbon density in this model [1,5]. The formula used is as follows.

$${\mathrm{C}}_{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}^{\mathrm{k}}={\mathrm{C}}_{\mathrm{v}\mathrm{e}\mathrm{g}\mathrm{e}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}^{\mathrm{k}}+{\mathrm{C}}_{\mathrm{s}\mathrm{o}\mathrm{i}\mathrm{l}}^{\mathrm{k}}$$

(4)

$${\mathrm{C}\mathrm{S}}_{\mathrm{x}} ={\sum }_{\mathrm{k}=1}^2{(\mathrm{A}}_{\mathrm{k}}\times {\mathrm{C}}_{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}^{\mathrm{k}})$$

(5)

In Equation (4), ${\mathrm{C}}_{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}^{\mathrm{k}}$, ${\mathrm{C}}_{\mathrm{v}\mathrm{e}\mathrm{g}\mathrm{e}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}^{\mathrm{k}}, {\mathrm{C}}_{\mathrm{s}\mathrm{o}\mathrm{i}\mathrm{l}}^{\mathrm{k}}$ are the total, vegetation, and soil carbon densities of the sites, respectively, and k represents the two different land-use types, forest and garden lands. Equation (5) indicates the land-use type k change area.

3.2.3. The Effectiveness in Each Project Stage

This study is based on the three main stages of the demolition and reclamation policy, with four types of CS indicators set. The differences and ratios between these indicators are used to analyze the CS outcomes at each stage. The difference indicators show the absolute deviation in CS, while the ratios measure relative efficiency. These indicators together form a framework to evaluate policy effectiveness and diagnose performance differences and areas for improvement.

$$\mathrm{T}\mathrm{h}\mathrm{e} \mathrm{e}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{s}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{P}\mathrm{F} \mathrm{s}\mathrm{t}\mathrm{a}\mathrm{g}\mathrm{e}\Rightarrow \left\{\begin{array}{c}{\Delta }_{\mathrm{x}\left(\mathrm{P}\mathrm{F}\right)}={\mathrm{C}\mathrm{S}}_{\mathrm{x}1}-{\mathrm{C}\mathrm{S}}_{\mathrm{x}2}\\ {\mathrm{R}\mathrm{A}\mathrm{T}\mathrm{I}\mathrm{O}}_{\mathrm{x}\left(\mathrm{P}\mathrm{F}\right)}={\displaystyle \frac{{\mathrm{C}\mathrm{S}}_{\mathrm{x}2}}{{\mathrm{C}\mathrm{S}}_{\mathrm{x}1}}}\end{array}\right.$$

(6)

$$\mathrm{T}\mathrm{h}\mathrm{e} \mathrm{e}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{s}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{C}\mathrm{O} \mathrm{s}\mathrm{t}\mathrm{a}\mathrm{g}\mathrm{e}\Rightarrow \left\{\begin{array}{c}{\Delta }_{\mathrm{x}\left(\mathrm{C}\mathrm{O}\right)}={\mathrm{C}\mathrm{S}}_{\mathrm{x}2}-{\mathrm{C}\mathrm{S}}_{\mathrm{x}3}\\ {\mathrm{R}\mathrm{A}\mathrm{T}\mathrm{I}\mathrm{O}}_{\mathrm{x}\left(\mathrm{C}\mathrm{O}\right)}={\displaystyle \frac{{\mathrm{C}\mathrm{S}}_{\mathrm{x}3}}{{\mathrm{C}\mathrm{S}}_{\mathrm{x}2}}}\end{array}\right.$$

(7)

$$\mathrm{T}\mathrm{h}\mathrm{e} \mathrm{e}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{s}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{P}\mathrm{M} \mathrm{s}\mathrm{t}\mathrm{a}\mathrm{g}\mathrm{e}\Rightarrow \left\{\begin{array}{c}{\Delta }_{\mathrm{x}\left(\mathrm{P}\mathrm{M}\right)}={\mathrm{C}\mathrm{S}}_{\mathrm{x}3}-{\mathrm{C}\mathrm{S}}_{\mathrm{x}4}\\ {\mathrm{R}\mathrm{A}\mathrm{T}\mathrm{I}\mathrm{O}}_{\mathrm{x}\left(\mathrm{P}\mathrm{M}\right)}={\displaystyle \frac{{\mathrm{C}\mathrm{S}}_{\mathrm{x}4}}{{\mathrm{C}\mathrm{S}}_{\mathrm{x}3}}}\end{array}\right.$$

(8)

In Equations (6)–(8), ${\mathrm{C}\mathrm{S}}_{\mathrm{x}1}$–${\mathrm{C}\mathrm{S}}_{\mathrm{x}4}$ are the potential, register, acceptance and verified CS for County X, respectively. ${\Delta }_{\mathrm{x}\left(\mathrm{P}\mathrm{F}\right)}, {\Delta }_{\mathrm{x}\left(\mathrm{C}\mathrm{O}\right)} \mathrm{a}\mathrm{n}\mathrm{d} {\Delta }_{\mathrm{x}\left(\mathrm{P}\mathrm{M}\right)}$ represent the absolute differences at the PF, CO, and PM stages, where a positive value indicates overachievement and a negative value indicates underperformance. ${\mathrm{R}\mathrm{A}\mathrm{T}\mathrm{I}\mathrm{O}}_{\mathrm{x}\left(\mathrm{P}\mathrm{F}\right)}, {\mathrm{R}\mathrm{A}\mathrm{T}\mathrm{I}\mathrm{O}}_{\mathrm{x}\left(\mathrm{C}\mathrm{O}\right)}$ and ${\mathrm{R}\mathrm{A}\mathrm{T}\mathrm{I}\mathrm{O}}_{\mathrm{x}\left(\mathrm{P}\mathrm{M}\right)}$ reflect the relative efficiency at the PF, CO, and PM stages, with R > 1 indicating positive effects and R < 1 suggesting losses. When Δ > 0 and R > 1, it indicates that the stage is operating well; otherwise, issues need to be traced for each stage.

4. Results

4.1. Analysis of Land-Use Change and Verified Carbon Storage of Demolition and Reclamation (D&R)

D&R primarily converts rural construction land into forest and garden land. For comparison, this study standardized six types of land-use change areas, including overall rural construction land, forest land, and garden land in Guangdong Province, as well as rural construction land, forest land, and garden land in D&R zones. Specifically, each land-use change area was divided by the total area of Guangdong Province.

The verified CS was calculated using Formula (2), which multiplies the verified area of forest and garden lands in each county by their respective carbon densities. According to

Table A3. Carbon storage of each demolition and reclamation county during each process.

Region	County	Verified Carbon Storage (t)	Qualified Carbon Storage (t)	Carbon Storage After Construction (t)	Maximum Ideal Carbon Storage (t)	The Quality of QV (%)	The Quality of CO (%)	The Quality of SD (%)
Pearl River Delta	Zengcheng	0.0	0.0	8.7	21,378.1	-	0.0%	0.0%
	Gaoming	86.1	114.8	340.1	9832.3	75.0%	33.7%	3.5%
	Xinhui	343.8	481.2	1609.5	17,655.1	71.5%	29.9%	9.1%
	Taishan	154.0	215.6	1925.5	30,267.5	71.5%	11.2%	6.4%
	Kaiping	367.0	513.7	2845.3	18,601.1	71.5%	18.1%	15.3%
	Heshan	244.8	342.7	1710.8	14,937.5	71.5%	20.0%	11.5%
	Enping	381.3	533.7	2538.5	16,063.3	71.5%	21.0%	15.8%
	Huiyang	0.0	0.0	512.2	25,803.1	-	0.0%	2.0%
	Boluo	87.2	113.4	804.4	31,685.6	76.8%	14.1%	2.5%
	Huidong	422.6	479.9	830.5	18,399.5	88.1%	57.8%	4.5%
	Longmen	456.3	695.4	1617.8	15,875.6	65.6%	43.0%	10.2%
Northern Guangdong	Wujiang	94.9	111.5	248.3	4949.3	85.1%	44.9%	5.0%
	Zhenjiang	58.2	88.4	225.9	7231.6	65.8%	39.1%	3.1%
	Qujiang	8.2	51.4	357.4	13,135.0	15.9%	14.4%	2.7%
	Shixing	213.7	292.4	1366.3	15,282.4	73.1%	21.4%	8.9%
	Renhua	110.5	161.1	508.7	12,027.6	68.6%	31.7%	4.2%
	Wengyuan	95.1	150.0	987.2	18,251.1	63.4%	15.2%	5.4%
	Ruyuan Yao Autonomous County	73.2	76.4	302.9	8515.5	95.8%	25.2%	3.6%
	Xinfeng	8.8	92.8	533.1	12,257.3	9.5%	17.4%	4.4%
	Lechang	285.4	349.8	610.6	13,710.5	81.6%	57.3%	4.5%
	Nanxiong	199.2	265.6	973.3	37,548.3	75.0%	27.3%	2.6%
	Dinghu	304.0	593.3	746.0	6681.2	51.2%	79.5%	11.2%
	Gaoyao	1514.5	2955.7	4948.6	40,871.1	51.2%	59.7%	12.1%
	Guangning	392.6	1018.7	6065.3	17,864.6	38.5%	16.8%	34.0%
	Huaiji	457.7	798.5	11,935.6	39,748.2	57.3%	6.7%	30.0%
	Fengkai	154.2	381.6	6139.9	21,387.2	40.4%	6.2%	28.7%
	Deqing	797.3	1160.6	3040.6	13,730.4	68.7%	38.2%	22.2%
	Sihui	662.6	1293.0	3257.2	16,028.3	51.2%	39.7%	20.3%
	Zijin	61.2	76.3	258.8	24,126.0	80.2%	29.5%	1.1%
	Longchuan	495.4	519.5	1041.5	33,365.6	95.4%	49.9%	3.1%
	Lianping	173.6	173.6	358.3	13,480.0	100.0%	48.5%	2.7%
	Heping	215.7	240.9	409.4	20,637.5	89.6%	58.8%	2.0%
	Dongyuan	99.1	171.7	2246.7	18,813.2	57.7%	7.6%	11.9%
	Qingcheng	5.5	8.0	256.0	23,038.2	69.1%	3.1%	1.1%
	Qingxin	65.0	136.3	354.1	17,546.0	47.7%	38.5%	2.0%
	Fogang	119.7	125.1	204.9	17,807.8	95.7%	61.0%	1.2%
	Yangshan	255.2	273.5	564.4	13,772.0	93.3%	48.5%	4.1%
	Lianshan Zhuang Yao Autonomous County	251.5	324.7	2446.8	8665.9	77.5%	13.3%	28.2%
	Liannan Yao Autonomous County	45.5	167.6	1166.2	4537.9	27.2%	14.4%	25.7%
	Yingde	64.2	64.2	5081.3	35,039.4	100.0%	1.3%	14.5%
	Lianzhou	753.5	1793.9	5322.2	13,907.9	42.0%	33.7%	38.3%
Eastern Guangdong	Longhu	0.0	10.1	27.1	7613.0	0.0%	37.3%	0.4%
	Jinping	0.0	35.0	35.9	2899.3	0.0%	97.5%	1.2%
	Haojiang	0.0	0.8	9.3	3010.1	0.0%	8.9%	0.3%
	Chaoyang	2.0	7.8	16.6	26,864.7	25.0%	47.3%	0.1%
	Chaonan	2.1	8.3	16.4	20,551.8	25.0%	50.7%	0.1%
	Chenghai	10.1	10.1	15.0	3712.3	100.0%	67.3%	0.4%
	Nanao	0.0	0.0	2.7	1463.8	-	0.0%	0.2%
	Meijiang	38.7	48.4	102.0	2435.1	80.0%	47.4%	4.2%
	Meixian	443.1	565.6	1049.5	30,552.4	78.3%	53.9%	3.4%
	Dapu	418.8	737.7	3292.6	17,213.9	56.8%	22.4%	19.1%
	Fengshun	601.8	627.4	969.9	22,167.2	95.9%	64.7%	4.4%
	Wuhua	799.1	800.6	897.5	61,841.1	99.8%	89.2%	1.5%
	Pingyuan	768.9	944.1	2080.0	15,674.9	81.5%	45.4%	13.3%
	Jiaoling	232.4	316.6	792.8	12,320.6	73.4%	39.9%	6.4%
	Xingning	615.0	961.0	1643.5	51,710.0	64.0%	58.5%	3.2%
	Haifeng	62.7	89.0	158.7	7849.9	70.5%	56.1%	2.0%
	Luhe	92.6	92.6	206.2	7971.8	100.0%	44.9%	2.6%
	Lufeng	0.0	0.0	9.9	9938.0	-	0.0%	0.1%
	Xiangqiao	0.0	0.0	111.3	4178.9	-	0.0%	2.7%
	Chao’An	35.0	51.2	284.4	18,608.4	68.5%	18.0%	1.5%
	Raoping	20.5	122.8	439.0	16,702.2	16.7%	28.0%	2.6%
	Rongcheng	0.0	59.8	113.2	16,948.8	0.0%	52.8%	0.7%
	Jiedong	54.5	138.8	162.2	36,941.8	39.3%	85.6%	0.4%
	Jiexi	117.9	287.3	776.4	7254.2	41.0%	37.0%	10.7%
	Huilai	40.4	127.1	152.8	15,047.7	31.8%	83.2%	1.0%
	Puning	173.0	369.5	1261.3	37,511.6	46.8%	29.3%	3.4%
Western Guangdong	Mazhang	0.0	0.0	28.8	40,684.5	-	0.0%	0.1%
	Suixi	435.5	1019.2	2685.7	72,525.2	42.7%	38.0%	3.7%
	Xuwen	165.2	165.2	331.6	69,375.2	100.0%	49.8%	0.5%
	Lianjiang	680.0	1168.2	15,446.4	90,810.8	58.2%	7.6%	17.0%
	Leizhou	425.3	638.0	3274.0	54,339.3	66.7%	19.5%	6.0%
	Maonan	67.1	139.2	1199.4	49,970.9	48.2%	11.6%	2.4%
	Dianbai	1121.9	1846.4	5054.7	107,624.8	60.8%	36.5%	4.7%
	Gaozhou	369.1	908.4	3609.9	115,058.5	40.6%	25.2%	3.1%
	Huazhou	1112.6	1453.6	6212.0	100,095.8	76.5%	23.4%	6.2%
	Xinyi	478.6	560.8	1269.1	70,905.7	85.4%	44.2%	1.8%
	Jiangcheng	0.0	0.0	31.5	14,883.1	-	0.0%	0.2%
	Yangdong	168.3	168.3	369.3	10,221.4	100.0%	45.6%	3.6%
	Yangxi	263.1	446.1	1204.0	9549.7	59.0%	37.1%	12.6%
	Yangchun	399.5	530.4	4314.3	43,808.5	75.3%	12.3%	9.9%
	Yuncheng	110.7	203.3	556.2	18,917.9	54.5%	36.5%	2.9%
	Yun’An	303.8	562.6	2841.4	15,479.9	54.0%	19.8%	18.4%
	Xinxing	779.9	844.1	2301.7	28,600.9	92.4%	36.7%	8.1%
	Yunan	601.6	737.1	2428.4	24,504.3	81.6%	30.4%	9.9%
	Luoding	1375.2	1645.3	4680.3	66,959.8	83.6%	35.2%	7.0%

- indicates counties that have project initiation areas but not acceptance areas.

, reclaimed land from D&R projects across the province generated a total of 359,288.20 tons of CS, indicating significant CS output from these areas. As illustrated in Figure 6b, the impact of D&R varies among counties due to the spatial clustering of increased forest and garden lands. At the county level, the western and eastern counties of Guangdong Province, especially those on the margins, saw more notable increases in forest and garden lands. In terms of reclamation structure (Figure 7b,c), the western region, such as Luoding, Dianbai, Huazhou, and Xinxing, tends to reclaim land as forests, while the northern region, such as Gaoyao, Sihui, and Lianzhou, favors gardens.

As shown in Figure 6a, during the three-year period from 2018 to 2021, when the D&R policy was widely implemented, rural construction land in Guangdong Province increased by 0.26‰, while forest and garden lands decreased by 0.19‰ and 0.21‰, respectively. However, the D&R policy led to a decrease of 0.17‰ in rural construction land, with an increase of 0.08‰ and 0.09‰ in forest and garden lands, respectively. This suggests that D&R effectively mitigated the reduction in forest and garden land and curbed the growth of rural construction land, which has a lower capacity for carbon sequestration. Thus, D&R contributes to increasing regional CS by improving land-use structure, bringing the region closer to its carbon neutrality goal.

4.2. Assessing the D&R Effectiveness at the Policy Formulation Stage

Potential areas in each county were allocated based on the proportions of forest and garden lands in the registered area of each county. The maximum ideal CS and the registered CS were then calculated using Formula (2), by multiplying the potential and registered areas of forest and garden lands by their respective carbon densities. The difference and ratio between the registered CS and the maximum ideal CS indicate the effectiveness of CS production at the PF stage, reflecting each region’s response to D&R.

At the provincial scale (Table A3), the registered CS accounts for only 5.81% of the maximum ideal CS, with a substantial gap of 36,272,530.12 tons. As shown in Figure 8g, most counties have ratios concentrated in the 0–10% range, highlighting significant potential for improvement in D&R. Among these, Huaiji (30.03%), Lianjiang (17.01%), and Yingde (14.50%) demonstrate high effectiveness, with their potential relatively well explored. Spatially (Figure 8a,d), counties with higher effectiveness are mainly located in Western and Northern Guangdong, including Gaozhou (111,448.56 tons), Dianbai (102,570.12 tons), Lianzhou (38.27%), and Guangning (33.95%).

4.3. Assessing the D&R Effectiveness at the Construction Stage

The accepted CS was obtained through Formula (2), which multiplies the accepted area of forest and garden lands in each county by their respective carbon densities. The margin and ratio between the registered CS and the accepted CS reflect the effectiveness of CS production at the CO stage.

In terms of scale (Table A3), the accepted CS accounts for only 24.71% of the registered CS. There is considerable disparity in CS achievement ratios among counties at this stage (Figure 8g), ranging from a high of 97.55% in Jinping to a low of 1.26% in Yingde. Spatially (Figure 8b,e), the effectiveness at this stage is high in counties within Eastern and Western Guangdong, including Lianjiang (214,065.42 tons, 7.56%), Yingde (75,217.86 tons, 1.26%), Huaiji (166,972.02 tons, 6.69%), and Qingcheng (3718.85 tons, 3.11%), which exhibit high margins and low ratios.

4.4. Assessing the D&R Effectiveness at the Post-Construction Management Stage

The margin and ratio between the verified CS and the accepted CS of D&R projects indicate the effectiveness of CS realization at the PM stage. According to Table A3, the overall ratio of verified CS to accepted CS is 65.03%, with a margin of 193,238.52 tons. While this margin is smaller than in previous stages, it still suggests room for optimization.

Spatially (Figure 8c,f), counties in the east demonstrate higher effectiveness at the PM stage compared to those in the west. The eastern counties have adopted comprehensive maintenance strategies for reclaimed plots, significantly reducing quality issues in project areas. For example, Lianping (0 tons, 100%), Chenghai (0 tons, 100%), and Luhe (0 tons, 100%) have achieved high CS realization. In contrast, western counties face various quality issues, such as Lianzhou, where the CS achievement ratio is only 42.00%.

5. Discussion

5.1. D&R Substantially Increased Regional Carbon Storage Compared with Other Land Consolidation Forms

D&R increased the area of ecological land by 3094 hm², CS. The CS increased by approximately 359,000 tons, equating to 6 tons per hectare. This is markedly higher than the CS increase seen in the Boundless Expanse of Fertile Farmland Construction policy, which yielded less than 1 ton per hectare [8,14]. D&R, which converts rural construction land into gardens and forests, shows a greater impact on regional CS due to its higher post-transformation carbon density compared to other LC forms [30]. The effectiveness at the PF, CO, and PM stages was 5.81%, 24.71%, and 65.03%, respectively, with discrepancies reflecting the nonlinear nature of policy dissemination.

Empirical findings suggest that forest land reclamation has a higher effectiveness of CO and PM processes compared to garden land reclamation. This highlights the importance of prioritizing forest land reclamation for enhancing regional CS and ensuring proper post-management.

5.2. Policy Formulation Stage: Significant Room for CS Improvement Suggests Low Adaptability of Policy Content

CS after construction reached only 5.81% of the maximum ideal, revealing significant systemic bottlenecks. One such bottleneck is the 400 m² minimum project area threshold, which, while ensuring administrative feasibility, excludes fragmented high-carbon-potential plots identified in our drone surveys. This represents a spatial trade-off between management convenience and the need for ecological connectivity, which limits the potential for carbon storage gains in fragmented areas [55,79]. Negative feedback systems are especially prevalent in counties with financial constraints, preventing the full utilization of rural construction land. These counties require alternative funding sources, such as social capital, to enhance CS [80]. Additionally, the minimum project area threshold of 400 square meters was not reasonably formulated, as many projects on undulating landscapes and with poor accessibility are smaller but effective in reducing landscape fragmentation and increasing CS [81].

Furthermore, the preservation of cultural services complicates the implementation process. In our study, counties with low D&R effectiveness, like Chenghai (0.40%) and Haifeng (2.02%), resisted the demolition of ancestral homes due to spiritual values, reflecting a dilemma seen in other countries. For instance, in Vietnam, carbon gains decreased by 9–15% when cultural landscapes were preserved [82]. This demonstrates the challenge of balancing carbon storage objectives with cultural preservation, highlighting the need for more adaptable policies that account for these trade-offs [83].

However, innovative solutions such as digital tools show untapped potential in resolving these conflicts. Guangning (33.95%), Fengkai (28.71%), and Lianjiang (17.01%) exemplify how digital platforms can help bridge this gap. These counties’ blockchain-based land credential system enhanced transparency and, through 3D spatial mapping, successfully preserved eight cultural heritage sites. This example underscores the potential for digital tools to reconcile ecological goals with cultural preservation, suggesting that more widespread use of such tools could mitigate implementation friction and improve overall D&R effectiveness [84].

5.3. Construction Stage: Detailing Work Contents and Unified Administration Addresses the Composite and Emergent Nature of Engineering

The effectiveness of CS during the CO varied significantly, from 97.55% to 1.15%. Counties like Jinping (97.55%) and Jiedong (85.56%) achieved high quality by detailing work contents, clarifying duties, and specifying project cycles, which reduced duplication and omissions, expediting implementation and enhancing CS [85]. Cooperative implementation, such as unified administration, also contributed to higher CS output [86], as seen in Dianbai (36.53%), where special products and under-forest beekeeping were cultivated.

Low-quality outcomes were often due to ambiguous implementation approaches [87], like verbally promised contents that were unfulfilled or opposed to the policy, as seen in Fogang (1.15%), where failed promises led to protests and hindered project progress. Therefore, detailing work contents and unified administration are crucial for improving CS. Lessons from high-quality counties should be summarized and applied to counties with low quality to enhance CS.

5.4. Post-Management Stage: Scientific Post-Management Is Crucial for Enhancing CS

Counties with high and relatively high effectiveness were primarily due to clear post-management responsibilities, consistent investment, and the use of market-based techniques that stabilized CS output. First, assigning clear post-management roles ensures timely repair work, enhancing plant survival rates [88]. Second, consistent investment in post-management allows local governments to allocate funds in advance, ensuring sustained maintenance and stable curing cycles [89]. This approach has led to significant vegetation growth and increased CS in areas like Chenghai (100.00%) and Luhe (100.00%). Third, counties applying market-oriented allocation maintained high CS output during the QV process, as market factors provided mature planting techniques [9] and benefited barren areas [90], as demonstrated in Lechang (81.59%).

Conversely, low-effectiveness outcomes were often due to inadequate post-management, leading to issues like weed infestation and rainwater retention, which hindered sapling growth and CS. For instance, plots in Qingcheng (47.71%) were located near steep hills, leading to the spread of climbing creepers and the diversion of soil nutrients, impeding sapling growth. Additionally, Guangdong Province’s undulating, wet, and rainy terrain makes reclaimed plots susceptible to waterlogging [91]. Uneven surfaces can form potholes or pools during rain, exposing the ground and damaging root systems, ultimately leading to vegetation loss and reduced CS [92]. Counties like Fenkai (27%) have faced these challenges. Therefore, improving post-management practices such as regular weed clearing, land leveling, and exploring more efficient market-based management models is crucial for enhancing CS growth.

5.5. Carbon Density Modified

Carbon density varies significantly among regions, with Western Guangdong having the highest and the PRD the lowest. This variation is influenced by hydrothermal conditions, economic development, and LC effectiveness [76,93]. The statistical results show that the standard deviation (SD = 2.01) and coefficient of variation (CV = 51.74%) of vegetation carbon density in forest land are significantly lower than those in garden land (SD = 2.97, CV = 113.28%), indicating that carbon density in forest systems is relatively stable, while garden land is more affected by human management or environmental disturbances, resulting in higher data dispersion (

Table 3. Carbon density of forest and garden lands (t/hm²).

Area	Forest Land				$\mathbf{Garden} \mathbf{Land}$
	${\mathbf{C}}_{\mathbf{v}\mathbf{e}\mathbf{g}\mathbf{e}\mathbf{t}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n}}$ *	${\mathbf{C}}_{\mathbf{s}\mathbf{o}\mathbf{i}\mathbf{l}}$ *	${\mathbf{C}}_{\mathbf{t}\mathbf{o}\mathbf{t}\mathbf{a}\mathbf{l}}$ *	$\mathbf{CV} {\mathbf{C}}_{\mathbf{v}\mathbf{e}\mathbf{g}\mathbf{e}\mathbf{t}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n}}$	${\mathbf{C}}_{\mathbf{v}\mathbf{e}\mathbf{g}\mathbf{e}\mathbf{t}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n}}$	${\mathbf{C}}_{\mathbf{s}\mathbf{o}\mathbf{i}\mathbf{l}}$	${\mathbf{C}}_{\mathbf{t}\mathbf{o}\mathbf{t}\mathbf{a}\mathbf{l}}$	$\mathbf{CV} {\mathbf{C}}_{\mathbf{v}\mathbf{e}\mathbf{g}\mathbf{e}\mathbf{t}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n}}$
Pearl River Delta	3.98	3.33	7.31	77.58%	0.57	2.97	3.54	78.66%
Northern Guangdong	3.98	5.52	9.5	34.63%	1.21	5.37	6.58	69.96%
Eastern Guangdong	3.98	4.64	8.62	58.01%	1.4	4.64	6.04	56.02%
Western Guangdong	3.98	5.09	9.07	51.41%	6.01	5.34	11.35	25.67%
SD ${\mathrm{C}}_{\mathrm{v}\mathrm{e}\mathrm{g}\mathrm{e}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}$ *	2.01				2.97
CV ${\mathrm{C}}_{\mathrm{v}\mathrm{e}\mathrm{g}\mathrm{e}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}$ *	51.74%				113.28%

* $C_{vegetation}$, $C_{soil}$ and $C_{total}$ represent vegetation, soil, and total carbon densities, respectively. SD $C_{vegetation}$ and CV $C_{vegetation}$ represent the standard deviation and coefficient of variation of vegetation carbon densities, which were calculated from Appendix A Table A1 and Table A2.

).

The CV of vegetation carbon density in forest land in the PRD is relatively low (CV = 77.58%), which may be related to its intensive economic activities and homogeneous land-use patterns. However, soil carbon density fluctuates significantly, reflecting the negative impact of pollutant interference and short-term-oriented ecological restoration LC measures on the stability of soil organic carbon [77]. In contrast, Western Guangdong shows the lowest coefficient of variation (CV = 51.41%) for forest land carbon density, with a clear concentration of data, likely due to favorable natural conditions and long-term sustainable LC practices. The extremely high CV in garden land (e.g., Western Guangdong CV = 25.67% vs. PRD CV = 78.66%) further confirms the regulatory role of different regional management strategies in stabilizing carbon density [94]. The short-term, economic-oriented agricultural model in PRD exacerbates carbon density fluctuations [75]. Meanwhile, in Western Guangdong, the D&R project integrates ecological and economic functions, not only increasing carbon density, with a difference of 9.57 t/ha², surpassing the 6.8 t/ha² benchmark of the 4p1000 Initiative [95], but also reducing variability through voluntary maintenance by villagers.

This statistical analysis provides quantitative support for the driving mechanisms behind regional differences. Although tropical rainforest carbon density remains a global focus, this study suggests that the subtropical agricultural–urban interface, which accounts for certain rate of the world’s habitable land, can balance development and carbon sequestration through targeted LC policies, such as D&R. However, attention should be given not only to mean differences but also to internal variability. For instance, the high CV value (113.28%) of garden land in the Pearl River Delta warns of the vulnerability of its carbon sequestration function, while the low CV value in Western Guangdong highlights the effectiveness of systematic management. Future policies should prioritize reducing variability in high-variation areas and enhance the stability of carbon density through long-term LC mechanisms, providing a reference for climate governance in the global urbanization process [89].

5.6. Limitations and Future Research

The multi-stage assessment framework developed in this study can quantify the effectiveness of CS changes and provide feedback on issues encountered during the LC process. However, this study has several limitations. (1) The sample size for data collection was small, with only 120 and 20 plots from the verification report and field research, respectively, despite over 6000 plots involved in D&R. (2) The framework design may not fully reflect LC’s impact on CS, as it excludes subtle anthropogenic activities like gasoline consumption during field research. Future studies should incorporate these impacts to create a more comprehensive assessment framework. (3) The quality definition used was overly simplistic, focusing only on the margin and ratio of completion. Future research should establish a baseline for quality measurement, such as the number of managers and weighted designs, to form an efficiency calculation model [96,97]. (4) Additionally, this study’s framework centers on carbon provisioning services but overlooks other ecosystem services that are crucial for a holistic assessment. These include cultural services, such as the preservation of spiritual landscapes, soil microbiome-mediated nutrient cycling, and the potential impacts of tourism on land values. By expanding the scope to include these ecosystem services, future studies will provide a more comprehensive framework for assessing the multifaceted impacts of LC on regional CS and other environmental services.

6. Conclusions

This study evaluates the impact of LC on CS through a case study in Guangdong Province. The results demonstrate a significant positive effect of LC on regional CS, especially at different stages: PF, CO, and PM. Specifically, the LC project increased forest and garden lands by 1420 hm² and 1674 hm², respectively, leading to a regional CS increase of 359,000 t, a five-fold rise per hectare.

At the PF stage, the effectiveness of CS was 5.81%, indicating low adaptability of the policy content, with substantial room for improvement. At the CO stage, the effectiveness of CS was 24.71%, with considerable variation between counties, ranging from 1.26% to 97.55%, highlighting the influence of executor capability and regional topographical differences on CS. The effectiveness of CS at the PM stage was 65.03%, with eastern counties showing lower CS growth compared to western counties, emphasizing the critical role of scientific post-management in enhancing CS.

These findings suggest that LC can significantly enhance regional CS, particularly in the PM stage. To improve CS outcomes, future efforts should focus on strengthening policy adaptability, optimizing implementation strategies, and enhancing the scientific management of the post-construction phase. Overall, this study provides theoretical and empirical support for optimizing LC processes, improving CS, and advancing regional carbon neutrality goals.