Urban agglomeration is the key area to realizing regional sustainable development. Timely and accurate assessment of its ESV spatial transfer can provide a scientific basis for intercity environmental cooperation to solve transboundary environmental problems. The ESV and its spatial transfer characteristics in the Central Plains Urban Agglomeration in 2000 and 2018 were quantified by introducing the breaking point model. The findings were as follows: Firstly, taking the central city of Zhengzhou as the transferred-in area, ESV spatial transfer distributions and changes presented a trend of hinterland > metropolitan area. Secondly, the ESV spatial transfer intensity from the metropolitan area to the central city presented an increase trend, with an increase of RMB 498,400–1,053,000/km2, and the ESV spatial transfer intensity from the hinterland to the central city presented a decrease trend, with a decrease of RMB 15,200–814,000/km2 in contrast. Thirdly, a total of RMB 294.763–331.471 billion worth of ESV has been transferred, and only that worth RMB 0.534–1.716 billion reached the central city, accounting for 0.181–0.518% of the total ESV transferred and 2.760–17.482% of the central city’s ESV. Fourthly, the ESV spatial transfer radius of each city was 25.47–214.17 km, but the ESV spatial transfer range of a few cities could reach the central city. Lastly, there was inefficiency in the ESV spatial transfer only in the natural driving spatial transfer pattern due to the spatial heterogeneity of ESV distribution, and there was potential for strengthening the ecological interactions based on space guidance provided by ESV spatial transfer.

Ecosystem service spatial transfer is considered a feature that can deliver ecosystem services at a distance to meet the demands in areas with uneven spatial distribution of natural and social economic development. The natural ES spatial transfer distance and intensity were simulated by using the modified breaking point model in the Central Plains urban agglomeration (CPUA) with the cities of Luoyang, Zhengzhou, Shangqiu, and Huaibei stretching across. It is shown that there is a spatial mismatch between ES supply from ecospace and its demands from cities; relying only on natural spatial transfer, none of the ESs of the ecospace can be transported to corresponding population centers; and a spatial gap between ES supply and demand is illustrated in urban agglomeration areas. Intercity cooperation in ecosystem management and landscape planning based on ES spatial transfer would be good choices for cities, giving full play to comparative advantages to achieve sustainable development for the entire CPUA.

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By using the methods of scenario analysis, model simulation, and the multi-objective spatial optimisation algorithm Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Pareto optimal solutions for water supply, water purification (N retention), as well as carbon storage and sequestration service (carbon service) of the Central Plains Urban Agglomeration (CPUA) were sought by adjusting the land use structure. It showed that, to reach the Pareto optimal solution goal, (1) in Scenario 1 (S1), the water supply service needs to increase by 10.682 billion cubic metres, the water purification (N retention) service needs to decrease by 11,400 tons, and the carbon service need to decrease by 2.487 billion tons. In Scenario 2 (S2), the water supply service needs to increase by 8.243 billion cubic metres, the water purification (N retention) service needs to decrease by 11,000 tons, and the carbon service needs to decrease by 2.466 billion tons. In Scenario 3 (S3), the water supply service needs to increase by 4.089 billion cubic metres, the water purification (N retention) service needs to decrease by 10,800 tons, and the carbon service needs to decrease by 2.380 billion tons. (2) After land use optimisation and adjustment, the S3 ecological land structure is complete and consistent with the vision of ecological protection and urban development in the study area, which is the optimal scenario. (3) Optimising the ecosystem service supply pattern through land use structure adjustment could balance the overall ecosystem service supply pattern of the study area In regions wherein ecosystem supply is insufficient and there is a spatial mismatch between supply and demand for ecosystem services, this study can guide regional land planning and assist in the formulation of ecosystem service management policies.

Based on multi-source remote sensing data, scenario analysis, and model simulation, the Pareto optimal solutions for water supply, water purification (N retention), and carbon storage and sequestration services under different scenarios were sought by adjusting its land use structure. The results showed that. In Scenario 1(S1), the water supply service needs to increase by 86.820 to 11.211 billion cubic metres, the water purification (N retention) service needs to decrease by 11,400 to 11,700 tons, and the carbon storage and sequestration service need to decrease by 2.070 to 2.487 billion tons. In Scenario 2(S2), the water supply service needs to increase by 8.243–10.666 billion cubic metres, the water purification (N retention) service needs to decrease by 11,300–1.10 million tons, and the carbon storage and sequestration service needs to decrease by 2.033 to 2.466 billion tons. In Scenario 3 (S3), the water supply service needs to increase by 7.832–11.437 billion cubic metres, the water purification (N retention) service needs to decrease by 1.16–10,800 tons, and the carbon storage and sequestration service needs to decrease by 19.220 to 2.380 billion tons. After land use optimisation and adjustment, the S3 ecological land structure is complete and consistent with the vision of ecological protection and urban development in the study area, which is the optimal scenario. After optimising the S3 ecosystem service supply pattern, the water supply, water purification (N retention), and carbon storage and sequestration services could connect the western and eastern ecosystem service supply areas, balance the overall ecosystem service supply pattern of the study area and meet the demand for ecosystem services. The results can guide regional land planning and ecosystem service management optimisation.