Optimizing land cover planning for carbon neutrality: Policy insights from scenario-based vegetation carbon sink assessment in the Guangdong-Hong Kong-Macao Greater Bay Area, China

doi:10.1016/j.regsus.2026.100368

Abstract

Abstract:

Mitigating national climate change requires achieving carbon neutrality, necessitating innovative land cover strategies that reconcile economic development with enhanced carbon sinks, particularly in rapidly urbanizing megaregions such as the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China. This study developed a novel assessment framework that integrates Multi-ObjectiveProgramming (MOP) land cover simulations with a Carbon Neutrality Effect (CNE) matrix and a neighborhood proxy method. This framework provides a quantitative evaluation of vegetation carbon sink dynamics resulting from land cover transitions during 2020-2060 under four scenarios: economic development first (ED), carbon neutrality first (EC), sustainable development (EDC), and natural development (EN). Model validation achieved high accuracy (Kappa coefficient=0.77, Overall Accuracy (OA)=0.84). Projected land cover changes revealed distinct transition patterns. Specifically, under the ED and EN scenarios, land cover changes will dominate by the expansion of urban area at the losses of grassland and crop land; under the EC scenario, the new forested land is primarily from crop land and grassland; and under the EDC scenario, urban area and forested land will achieve simultaneous expansion. Moreover, this study found the net vegetation carbon losses of 51.43×10¹⁰ and 274.54×10¹⁰ g C under the ED and EN scenarios, respectively, undermining carbon neutrality. In contrast, there will be a significant net carbon gain (64.52×10¹⁰ g C) under the EC scenario. Crucially, the EDC scenario demonstrates a viable balance, minimizing vegetation carbon loss (7.00×10¹⁰ g C) while substantially enhancing both socioeconomic benefit (1.34×10¹² CNY/a) and vegetation carbon sink benefit (0.15×10¹² CNY/a). This study underscores the critical role of optimized land cover planning in fostering synergistic economic growth and carbon neutrality. The findings provide actionable insights for the implementation of ‘Dual Carbon’ strategy in the GBA and serve as a valuable reference for sustainable land management in climate-vulnerable coastal megacities worldwide.

Key words: Multi-Objective Programming (MOP), Carbon neutrality effect (CNE) matrix, Economic development first (ED), Carbon neutrality first (EC), Sustainable development (EDC), Natural development (EN), Guangdong-Hong Kong-Macao Greater Bay Area (GBA)

WEN Youyue, CHEN Zhichao, CAI Saoman, YANG Jian. Optimizing land cover planning for carbon neutrality: Policy insights from scenario-based vegetation carbon sink assessment in the Guangdong-Hong Kong-Macao Greater Bay Area, China[J]. Regional Sustainability, 2026, 7(4): 100368.

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Land cover type	Socioeconomic benefit per unit (×10⁴ CNY/hm²)	Carbon sink per unit (×10⁴ g C/m²)	Carbon sink benefit per unit (×10⁴ CNY/hm²)
Crop land	10.45	11.21	79.99
Forested land	0.90	22.82	162.88
Grassland	3.61	7.87	56.15
Shrub land	2.26	9.88	70.49
Wetland	4.82	1.43	10.24
Water body	6.86	0.00	0.00
Urban area	1033.68	0.00	0.00
Bare land	0.00	0.00	0.00

Scenario	Year	Crop land (×10⁴ hm²)	Forested land (×10⁴ hm²)	Grassland (×10⁴ hm²)	Shrub land (×10⁴ hm²)	Wetland (×10⁴ hm²)	Water body (×10⁴ hm²)	Urban area (×10⁴ hm²)	Bare land (×10⁴ hm²)
Current value	2020	107.87	271.22	30.40	7.39	0.14	44.67	90.88	0.64
EN scenario	2030	93.70	258.43	26.65	6.83	0.16	43.44	123.42	0.57
	2040	83.60	245.94	24.47	6.44	0.16	41.78	150.28	0.53
	2050	76.36	234.19	23.06	6.17	0.16	40.01	172.75	0.50
	2060	71.11	223.37	22.05	5.96	0.15	38.32	191.75	0.48
ED scenario	2030	104.32	272.56	19.99	5.91	0.14	44.67	105.60	0.00
	2040	101.80	272.56	18.35	4.73	0.14	44.67	110.95	0.00
	2050	99.99	272.56	17.30	3.78	0.14	44.67	114.76	0.00
	2060	98.68	272.56	16.54	3.08	0.14	44.67	117.53	0.00
EC scenario	2030	104.32	287.29	19.99	5.91	0.14	44.67	90.88	0.00
	2040	101.80	292.63	18.35	4.73	0.14	44.67	90.88	0.00
	2050	99.99	296.45	17.30	3.78	0.14	44.67	90.88	0.00
	2060	98.68	299.27	16.54	3.03	0.14	44.67	90.88	0.00
EDC scenario	2030	104.32	273.89	19.99	5.91	0.14	44.67	104.28	0.00
	2040	101.80	276.55	18.35	4.73	0.14	44.67	106.96	0.00
	2050	99.99	279.20	17.30	3.78	0.14	44.67	108.12	0.00
	2060	98.68	281.86	16.54	3.03	0.14	44.67	108.29	0.00

Period	Land cover type	Crop land (×10¹⁰ g C)	Grassland (×10¹⁰ g C)	Shrub land (×10¹⁰ g C)	Bare land (×10¹⁰ g C)	Sum (×10¹⁰ g C)	Net_in (×10¹⁰ g C)
2020-2030	Forested land	1.91	0.82	0.32	0.02	3.08	3.08
2020-2030	Urban area	-3.69	-21.70	-2.27	0.94	-26.72	-26.72
2030-2040	Urban area	-3.38	-4.61	-2.61	-	-10.59	-10.59
2040-2050	Urban area	-2.51	-3.42	-2.09	-	-8.02	-8.02
2050-2060	Urban area	-1.82	-2.80	-1.47	-	-6.09	-6.09
2020-2060	Forested land	1.91	0.82	0.32	0.02	3.08	-48.35
	Urban area	-11.41	-32.52	-8.43	0.94	-51.43	-48.35
	Net_out	-9.49	-31.71	-8.11	0.96	-48.35

Period	Land cover type	Crop land (×10¹⁰ g C)	Grassland (×10¹⁰ g C)	Shrub land (×10¹⁰ g C)	Bare land (×10¹⁰ g C)	Sum (×10¹⁰ g C)	Net_in (×10¹⁰ g C)
2020-2030	Forested land	9.67	16.46	4.16	4.33	34.63	34.63
2030-2040	Forested land	6.98	2.71	3.39	-	13.08	13.08
2040-2050	Forested land	5.03	1.78	2.76	-	9.58	9.58
2050-2060	Forested land	3.65	1.30	2.27	-	7.22	7.22
2020-2060	Forested land	25.34	22.26	12.59	4.33	64.52	64.52
2020-2060	Net_out	25.34	22.26	12.59	4.33	64.52

Period	Land cover type	Crop land (×10¹⁰ g C)	Grassland (×10¹⁰ g C)	Shrub land (×10¹⁰ g C)	Bare land (×10¹⁰ g C)	Sum (×10¹⁰ g C)	Net_in (×10¹⁰ g C)
2020-2030	Forested land	3.27	1.84	0.66	0.06	5.82	-18.22
2020-2030	Urban area	-2.99	-19.94	-2.03	0.93	-24.03	-18.22
2030-2040	Forested land	2.33	2.04	0.92	-	5.28	0.87
2030-2040	Urban area	-2.16	-0.44	-1.80	-	-4.41	0.87
2040-2050	Forested land	1.76	1.48	2.58	-	5.82	4.30
2040-2050	Urban area	-1.52	0.00	0.00	-	-1.52	4.30
2050-2060	Forested land	3.11	1.07	2.09	-	6.28	6.05
2050-2060	Urban area	-0.22	0.00	0.00	-	-0.22	6.05
2020-2060	Forested land	10.46	6.42	6.25	0.06	23.20	-7.00
	Urban area	-6.90	-20.39	-3.83	0.93	-30.19	-7.00
	Net_out	3.56	-13.96	2.42	0.99	-7.00