Efficiency hierarchy and optimization of waste incineration in China to balance disposal and energy supply

Domain: chemistry
Published: 2026-02-23
Editorial outcome: major_revision

Abstract
Waste-to-energy incineration addresses the dual pressures of municipal solid waste growth and energy shortages. Persistently low energy efficiency has limited its role as energy sources. We compile a national database of 975 plants including 2151 incinerators across China, showing that they supplied up to 7% of residential electricity consumption in 2023. A three-tier classification framework was established based on energy efficiency performance to distinguish disposal-oriented, energy-recovery, and green energy plants. Scenario projections under five Shared Socioeconomic Pathways indicated that waste incineration could generate up to 259 TWh by 2035, meeting 13% of residential demand. Artificial intelligence-based control and waste heat recovery are the most cost-effective. Efficiency improvements could mitigate up to 60% of greenhouse gas and flue-gas pollutant emissions relative to the 2035 baseline. These findings could redefine the role of waste incineration in low-carbon energy systems and provide scalable insights for emerging economies undergoing waste and energy transitions.

Introduction
Global municipal solid waste (MSW) is expected to surge from 2.2 billion tons in 2023 to 3.8 billion tons in 2050, further aggravating the triple planetary crises of climate change, biodiversity loss, and pollution1. Global energy demand is projected to nearly double by 2050, particularly in rapidly urbanizing and industrializing regions such as China, Southeast and South Asia, and Sub-Saharan Africa2, where also face persistent energy shortages, over 90% of people without electricity access reside in these areas3. Thus, energy shortages and booming MSW generation present interconnected global challenges. Global MSW contains considerable energy, estimated at approximately 13.8–32.2 exajoules in 2023 worldwide, and is projected to contribute about 3–7% of global primary energy over the next two decades within a circular society pathway4.

Modern incineration plants offer a localized and synergistic solution to both issues, by converting waste into energy via cogeneration and yielding both steam for district heating and grid-compliant electricity, as regions experiencing rapid MSW growth due to high population density often overlap with those suffering from inadequate energy supply. For example, in Bangladesh, a pilot waste-to-energy (WTE) incineration project demonstrated the feasibility of converting urban organic waste into electricity for local power grids, addressing both MSW pressure and energy deficits in urbanizing areas5. WTE has already become the predominant MSW disposal method in regions with high population density and land constraints, accounting for 83% of MSW treatment in China6, 80% in Japan7, and is increasingly being adopted in other countries and areas, such as those in Southeast Asia8 and Sub-Saharan Africa9, although it is not a fully sufficient solution for MSW disposal compared with compost or material reuse. While WTE enables simultaneous waste treatment and energy recovery, its relatively low energy efficiency remains a significant bottleneck, constraining its potential as a reliable source of sustainable energy. This technical limitation has fueled ongoing debate regarding whether WTE plants should be regarded primarily as waste disposal infrastructure or as energy-producing facilities.

The energy efficiency of WTE facilities is influenced by multiple factors, including MSW composition10, combustion process, energy recovery system and utilization scenarios11, flue gas pollutants (FGPs) control technologies and operational experience12. Higher moisture content and limited lower heating values (LHV) reduce combustion efficiency and increase auxiliary energy demand, thereby lowering overall energy performance13. The composition of MSW depends on living habits and economic conditions and has changed over time, as reflected by the increase in the national average LHV in China from approximately 4500 kilojoule (kJ) kg-1 in 2000 to 5500 kJ kg-1 in 202014. The waste management systems also significantly influence the MSW composition15. In recent years, large cities in several emerging market and developing countries (as defined by the International Monetary Fund16), such as Shanghai (China)17, Bangkok (Thailand)18, and Indore (India)19, have implemented source-separation policies. For example, in Shanghai, the removal of food waste reduced the moisture content of waste entering incinerators from approximately 57% (mixed waste) to 33%, and its LHV rose from about 5000–6700 kJ kg-1 to 8000–13,000 kJ kg-1. These changes in feedstocks over long operational periods, resulted in significant adaptability challenges for fixed technical parameters and device types, especially in those emerging market and developing countries20,21. In addition to the feedstock, energy recovery efficiency is affected by the incinerator type and scale effects, and both the local industry layout and climate properties influence the efficiency through different energy utilization scenarios.

Given the complexity of the socio-economic-technical systems involved, a robust assessment framework based on wide-range statistical results combining individual operation conditions is needed to accurately evaluate the current energy recovery efficiency and propose acceptable functional classification criteria.

China provides a compelling context for studying the impacts of diverse factors on energy recovery, over 83% of MSW in China is processed through 975 WTE plants comprising 2151 incineration lines in 2023. Significant regional disparities in economic development and waste management policies have resulted in substantial differences in waste composition. While some Coastal regions with higher gross domestic product per capita (GDP) and urbanization rate typically exhibit MSW LHV close to 8000 kJ kg-1, several Middle and Western regions frequently contend with low LHV (approximately 4000–5000 kJ kg-1). This variation presents a practical scenario for investigating the effects of changes in waste composition on energy recovery efficiency. These facilities also span a wide range of technical types and scales, from small county-level plants to large urban complexes, as well as diverse climatic zones ranging from tropical to cold. Comprehensive operational and combustion data have been systematically recorded and integrated into the national online monitoring system since 202022. This extensive dataset enables detailed analysis of operational performance and facilitates evaluation of future roles for WTE plants, offering valuable insights for similar developments in other emerging markets and developing countries.

To ensure comprehensive representativeness, a database covering all 2151 incineration lines across China was established, integrating technical information on furnace design, energy recovery systems, and flue gas treatment processes, as well as quantifying operational experience, waste composition data, and FGPs emission records. An analytical framework was developed to assess the current energy recovery performance and propose tailored strategies for enhancement, with the influence of various factors explicitly quantified. Tiered functional classification criteria for WTE plants were established based on energy recovery performance, and an efficiency correction matrix for passive influencing factors, such as plant size, third-party heat demand, and local oxygen content, was developed. Future energy recovery and optimization potential were simulated under five Shared Socioeconomic Pathways (SSPs), utilizing predictive models developed for future MSW generation and composition trends. A practical strategy comprising 3 categories and 11 energy recovery enhancement measures was proposed, and the potential benefit and cost-effectiveness of each measure was systematically evaluated. The synergistic benefits of mitigating both greenhouse gas (GHG) and FGPs emissions were examined. These findings could provide guidance for redefining the ecological niche of WTE plants within broader energy systems and advancing the objectives of the circular economy and the Sustainable Development Goals (SDGs) 7, 11, and 13.

Results
Regional imbalance for energy recovery of waste incineration in China
The gross electricity generated from WTE plants in China surged from 5 terawatt-hour (TWh) in 2010 to 120 TWh in 2023 (Supplementary Fig. 1), as the annual WTE capacity significantly increased from 14 million tons (Mt) in 2010 to 327 Mt in 2023. The net electricity was 99 TWh in 2023, after around 17% of total electricity was self-consumed for daily operation, which could contribute to around 7% of residential electricity consumption, whereas that in 2010 was only around 1%, even though the total residential electricity usage increased from 513 to 1352 TWh during this period. Approximately 42% of electricity was generated from biomass in MSW, such as food waste, paper, and wood, which was slightly lower than the corresponding 45% in the US, owing to their higher content of yard trimming waste and paper23. Spatial disparities were observed in net electricity generation (Fig. 1a). The Eastern Coastal and South Coastal regions were the main contributors, with approximately 21% and 20% of the net electricity generation, accounting for approximately 10% and 9% of local residential consumption, respectively. Cities with larger populations generate greater MSW treatment demands, necessitating a higher WTE capacity (R2=0.62, p<0.001) (Fig. 1b). Notably, among cities with comparable population sizes, those with more advanced economic development (indicated by higher urbanization rates and per capita GDP) exhibited greater WTE processing capacity.

The average electricity production rate was approximately 369 kilowatt-hour (kWh) per ton of MSW. They were lower than those in European Union (EU) countries (581 kWh t-1)24 and the United States (US) (555 kWh t-1)25, owing to the disparities in the corresponding LHV of MSW feeding, which was 3600–11,000 kJ kg-1 in China, 8000–11,000 kJ kg-1 in EU countries26, and 11,000–12,000 kJ kg-1 in the US27. A significant positive correlation was observed between the electricity generation rate and the feedstock LHV (R2=0.44, p<0.001) (Fig. 1c). The LHV is primarily affected by the moisture content, and the proportion of higher-calorific-value waste components, such as plastics and paper waste, which are strongly correlated with regional economic development28 and MSW management policy29. Economic growth links to lifestyle changes, such as the increased use of plastic and paper packaging, online retailing, and greater reliance on disposable items, all of which contribute high-LHV components to the MSW stream30. Over the past two decades, the LHV of MSW in China has increased by approximately 22%, driven by economic growth, and MSW from the economically advanced Southern and Eastern Coastal regions exhibit the highest shares of plastic and rubber waste, with average LHV levels approximately 6–11% higher than the national mean. With the implementation of source separation policies in prefecture-level cities, the LHV of incinerated waste increased by approximately 60–90%, as in the case of Shanghai, where food waste with high moisture content was removed.

Energy efficiency also plays a critical role in recovering the corresponding energy contained in the feedstock (Fig. 1c). The efficiency weighted by the equivalent factors of electricity and heat energy (EEef) was 0.55 with 95% confidence interval at 0.43–0.65 at the country level, which was close to the values of electricity-only plants (0.40–0.70), and was lower than those of combined heat and power (CHP) (0.59–1.10) and heat-only facilities (0.63–0.71) in other countries12,26,31–35. The highest EEef of 0.63 was found in the Middle Yellow River region, since 92% of WTE plants were new incinerators constructed after 2015, much higher than 65% and 77% of the Eastern Coastal and South Coastal regions. The regional differences phenomenon showed that power generation relied on economic first-mover advantage, while technological latecomer advantage preferred EEef improvement.

Impact factors of energy efficiency and corresponding effects
The EEef of WTE plants generally correlated with the technology applied in WTE plants, operational experience and local characteristics such as LHV and climate features. Incinerators were divided into four groups based on their treatment capacity per day, according to the technical requirements for MSW incinerators in China36, small-scale (<300 t d-1), middle-scale (300–600 t d-1), large-scale (600–750 t d-1) and super-large (>750 t d-1). The highest EEef (0.64) was observed in WTE plants with super-large incinerators (Fig. 2a), approximately 3.22%, 20.75%, and 36.17% higher than those of large, middle, and small-scale incinerators, respectively, which was mainly due to the higher thermal efficiency and reduced energy losses within the chamber of larger tonnages. These super-large incinerators were mainly deployed in major cities with higher MSW disposal demands. Steam temperature and pressure are also critical parameters for electricity generation. The EEef of the incinerator with sub-high pressure and medium-temperature steam (0.56) was approximately 22% higher than that of the system operating at general medium-temperature and medium-pressure conditions (400 oC, 4 MPa) (Fig. 2b). Both incinerator size and steam parameters have positive impacts on energy recovery, while partial energy needs to be consumed in the FGPs control processes to meet the emission limits. More complex FGPs control systems typically require greater energy input (Supplementary Fig. 2). For example, a seven-step configuration, which has been primarily implemented to meet ultra-low emission standards in certain coastal regions (e.g., Hainan Province and Shenzhen City), consumes 41% more energy than the commonly used five-step process (Fig. 2c).

The operational experience has rarely been considered for the assessment of energy recovery performance due to the lack of quantified data, while it has a significant impact on the combustion conditions and pollutants control37. We categorized practical operational effectiveness into three levels to evaluate the experience of stuff, by the quantified statistics integrating the temperature of flue gas in the chamber, frequencies and duration of incinerator shutdown (Supplementary Method 1, Supplementary Fig. 3). The mean EEef of incinerators (level I) with higher temperature, lower maintenance frequency and shorter time of shutdown was 9.26% and 25.53% higher than that of incinerators at level II and level III, respectively (Fig. 2d).

Local factors, including economic development level, waste management policies, and climatic conditions, also affect EEef by influencing MSW composition and the demand for mandatory heat supply. Both waste composition and energy utilization pathways substantially impact EEef by affecting the conversion efficiency between energy inputs and outputs. Although a higher LHV is generally associated with a greater electricity generation rate (Fig. 1c), we observed a notable negative correlation between the LHV of incoming MSW and the EEef of WTE plants (R2=0.41, p<0.001) (Fig. 2e), particularly in coastal regions. This pattern arises because the increasing LHV driven by economic growth and the implementation of waste separation in these areas often exceeds the design specifications for the thermal loads of incinerator grates and heat recovery boilers, resulting in operational mismatches.

Energy utilization is also strongly shaped by regional climate and industrial structure. In colder regions and industrial parks with heating requirements, waste heat from steam is preferentially recovered for thermal applications. CHP systems could achieve an EEef of approximately 0.90, representing a 64% improvement over electricity-only generation (Fig. 2f). In China, mandatory residential heating is primarily implemented in urban communities north of the Qinling-Huaihe Line, which separates the subtropical and temperate climate zones38 (Supplementary Fig. 4). Consequently, northern WTE plants typically have a higher demand for waste heat utilization than their southern counterparts. However, due to Not-In-My-Backyard opposition during facility siting, most Chinese WTE plants were built in suburban locations, unlike the urban-center siting common in facilities in Japan and EU countries39,40. This suburban placement, coupled with the early policy focus on rapidly expanding treatment capacity to address MSW disposal crises, has limited the availability of nearby heat recipients and thus constrained the deployment of CHP systems.

A special case occurs on the Qinghai-Tibet Plateau, where atmospheric pressure is about 60% of that in lowland regions, resulting in reduced oxygen availability. The Lhasa WTE plant in the Tibet Autonomous Region typically operates with an excess-air supply rate 3.5 times higher than that of low-altitude facilities, leading to greater heat loss via flue gas41. Lower ambient temperatures in high-altitude regions also contribute to enhanced furnace radiation losses. As a result, the Lhasa plant exhibited an EEef approximately 44% lower than comparable facilities with similar waste composition but located at lower altitudes.

Energy efficiency correction and tiered criteria for facility function
Electricity generated from WTE plants is currently classified as renewable energy in national statistics, although this categorization remains controversial. As carbon reduction frameworks worldwide continue to improve, there is an urgent need to establish a widely applicable, transparent standard for the clear classification of WTE facilities. Implementing energy efficiency as a certification threshold could incentivize plants to enhance their energy recovery performance, thereby promoting sustainable MSW management systems and facilitating energy sector decarbonization. To enable cross-regional benchmarking of energy recovery, we developed a correction factor matrix to generate an adjusted EEef (EEef-adjusted) (Supplementary Method 2). All passive influencing factors, such as site-specific climatic conditions (e.g., temperature and oxygen partial pressure) and combustion technology parameters, were normalized to ensure that all facilities were evaluated against a consistent baseline. For example, plants in warmer regions without heating demand are inherently limited in utilizing waste heat from steam generation; thus, direct efficiency comparisons with CHP-equipped facilities in colder regions would be inappropriate due to substantial differences in heat recovery potential.

The EEef-adjusted was proposed as a functional classification criterion for WTE plants, with tiered thresholds established based on operational data and international benchmarks (EU critical values and China’s Green Power Certification standards42). Three distinct categories were defined: MSW disposal facilities only (EEef-adjusted < 0.65), energy recovery plants (0.65 ≤ EEef-adjusted < 0.77) and green energy plants (EEef-adjusted ≥ 0.77). Based on this framework, 38% of the WTE plants functioned merely as MSW disposal facilities (Fig. 3b), and the top 20% WTE plants were recommended as green energy plants (72% concentrated in the Eastern Coastal and South Coastal regions). This classification system effectively eliminates the impact of uncontrollable variables, enabling equitable performance comparisons across diverse regions and technologies.

For existing facilities, EEef-adjusted is primarily influenced by feedstock quality and operational expertise. In coastal regions, elevated MSW LHV was the main constraint on EEef-adjusted performance (Supplementary Method 3). Targeted technological upgrades are therefore recommended, such as replacing air-cooled and insulated arches with water-cooled arches to improve thermal stability, and installing additional convection heat exchange surfaces in the secondary or tertiary flue to mitigate high-temperature corrosion, enhance steam utilization, and boost energy recovery. For inland facilities, improving operator expertise is critical for raising EEef-adjusted values. For new plants, large-scale incinerators equipped with high-pressure steam boilers are recommended, provided that waste generation volumes and investment conditions are sufficient.

Energy recovery and substitution potential in 2035
The energy recovery potential of WTE plants across five SSPs in 2035 was projected, based on predictive models of MSW generation and MSW composition at the provincial level and EEef at the plant level. Net energy generation is expected to increase to 161–164 TWh in 2035 across SSP1-5 under business-as-usual (BAU) scenarios (Fig. 4a), accounting for approximately 8.33–8.48% of residential electricity consumption. The energy recovery per ton of MSW will rise from 361 kWh t-1 in 2023 to 497–502 kWh t-1 in 2035, while the average EEef may decrease to approximately 0.43, given that the projected LHV across all five SSP scenarios is expected to reach 11,000 kJ kg-1 by 2035, driven by economic and social development, especially the increasing per capita disposable income and growing share of small-sized households. These economic and demographic changes are expected to boost the consumption of packaging-intensive products such as takeaway food, express deliveries, and frozen convenience goods, thereby increasing the share of plastic and paper waste in the MSW stream and elevating the overall heating value43. This value approaches or even exceeds the current LHV range of MSW in EU countries26 and the US27. However, it substantially exceeds the rated thermal capacity of existing WTE plants in China. Nearly all WTE facilities would require significant technological upgrades to meet energy recovery thresholds under these projected conditions.

Energy recovery enhancement (ERE) scenarios were developed based on the economic and demographic projections of the BAU scenarios, taking into account of facility retrofit to overcome the excessive heat load, improvements in operational performance, and CHP updates (Fig. 4b). These strategies were determined based on the energy recovery response, combining their associated implementation costs, local economic conditions and policy constraints (Supplementary Method 4, Supplementary Table 2). Energy recovery will rise by approximately 28–59% to 197–259 TWh and account for about 10.16–13.41% of residential electricity usage at ERE scenarios compared with that of BAU scenarios. The energy generation per ton of MSW varied from 605 kWh t-1 in SSP4 to 794 kWh t-1 in SSP1, with an average value of 700 kWh t-1 across all five SSP scenarios. Among the five SSP pathways, up to 65% and 33% of WTE plants, both in SSP1 are supposed to reach the certification limits for energy recovery and green energy, respectively. Facility retrofits to accommodate MSW with higher LHV can increase energy recovery potential by about 5–10%. Improvements in operational practices can significantly boost energy recovery potential by approximately 15–28%, helping to mitigate regional imbalances in EEef. Optimizing residual steam heat utilization, such as residential heating in northern regions and supplying industrial enterprises nationwide, could enhance energy recovery by approximately 6–13%.

Among these strategies, introducing artificial intelligence (AI)-assisted control models to improve operational performance is the most cost-effective per additional unit of recovered energy (Fig. 4b, Supplementary Table 2), which also shows at least 20% energy system stability44. Internal thermal cycle optimizations and waste heat recovery measures within WTE plants, such as organic Rankine cycles and flue-gas condensation heat recovery, rank second in terms of cost-effectiveness, as they maximize energy recovery from low-temperature flue gas. Although utilizing residual steam heat for district heating can significantly enhance EEef, its applicability is limited by the necessity of coupling with external demand and infrastructure, restricting the achievable improvement. Government subsidies commonly available for infrastructure that benefits local livelihoods provide policy support for upgrading WTE facilities to CHP systems45. In contrast to the voluntary implementation of operational improvements and CHP upgrades, retrofits addressing excessively high LHV of MSW are mandatory adaptations. Among these retrofits, enlarging heat-exchange surfaces and altering furnace-wall cooling mediums are more economically viable.

Co-benefits of greenhouse gas and flue gas pollutants reduction
WTE plants also release GHG and various FGP during the combustion process. Effective reduction is critical for the sustainable operation of incineration facilities, in addition to energy recovery improvements. The emission inventory was developed to evaluate the synergistic effect of EEef enhancement based on an incinerator-level emission factor framework. Approximately 302.94 CO-eq million tons (Mt) were emitted in 2023, and 163.67 Mt of CO2 were released from the combustion of biogenic MSW, which was considered as CO2 neutral, and without any negative influence on the climate (Supplementary Fig. 5). The rest part (139.28 Mt CO-eq) was designated as direct emission. The net GHG emission was 83.65 Mt CO-eq y-1, with 55.59 Mt CO-eq y-1 emitted from conventional power plants substituted by the equal electricity derived from MSW incineration. The emission factor of net CO-eq (EFCO2-eq) was 254 kg per ton MSW incinerated. Total net GHG emissions were mainly released from South Coastal, Eastern Coastal and North Coastal regions with 23 Mt CO-eq, 16 Mt CO-eq, and 14 Mt CO-eq, accounting for around 27%, 19% and 17%, respectively contributed by the higher WTE capacity and EFCO2-eq. The net GHG emissions are expected to grow around 16–18 Mt CO-eq in 2035 among five SSPs in BAU scenarios (Fig. 4c), since the MSW generation and proportions of the waste with more carbon content, such as plastic waste and paper waste, increase with economic development. While their emissions will decrease by 5–44 Mt CO-eq y-1 in ERE scenarios, led by the improved EEef and corresponding replaced emissions from the conventional power plant. The EFCO2-eq increased by 37–44% to 351–369 kg CO2-eq t-1 under BAU scenarios, and enhanced energy recovery potentials in ERE scenarios reduced EFCO2-eq by 82–214 kg CO2-eq t-1.

The total emissions of PM, CO, NOx, SO2, and HCl were approximately 2074 t y-1, 5149 t y-1, 88,115 t y-1, 18,039 t y-1, 8264 t y-1, respectively (Fig. 4c). The uncertainties are listed in Supplementary Table 3. The corresponding emission factors of FGPs were 6.35 g t-1, 15.76 g t-1, 269.80 g t-1, 55.23 g t-1, and 25.30 g t-1, which were 48–90% lower than the limits of the national emission standard (GB18485-2014)46. Significant negative correlations existed between these emission factors and electricity generation rate (Supplementary Fig. 6), since both better operational performance and higher combustion efficiency showed positive impacts on pollution control37 and energy recovery. The total emissions of SO2, NOx, and HCl are expected to increase approximately 13–37% in 2035 under BAU scenarios, owing to the rising emission factors37 and incinerated MSW. The total emissions of PM and CO were consistent with the emission levels of 2023, driven by the comprehensive impacts of decreasing emission factors and increasing amounts of burned MSW. Elevated LHV of MSW leads to increased furnace temperatures and localized overheating, resulting in higher thermal NOx emissions. Meanwhile, a higher proportion of paper and plastic components in the waste stream contributes to elevated sulfur and chlorine content, consequently increasing SO2 and HCl emission factors. Under the ERE scenarios, the total emissions of CO and NOx are projected to decrease by approximately 60% and 7% respectively, due to the optimized furnace combustion conditions. The robustness of the emission factors and the probability of exceeding the instantaneous concentration standards can be simultaneously reduced by around 40% leading to improved operational expertise.

Methods
Dataset of energy recovery, flue gas pollutants and greenhouse gas emissions from waste incineration plants in China
All 975 WTE plants, encompassing 2151 processing lines, were operational in China at the end of 2023. A dataset was established, including technical and operational information, energy input and output data, emission data related to FGPs and GHG, and provincial-level demographic and economic parameters. The technical information (incinerator, energy recovery, and FGPs control) and operational conditions (chamber temperature, shut-down frequencies and durations) at the incinerator level were collected from the national platform and TOP companies in WTE industry of China, including Everbright Environment, Shanghai Environment Protection Group Co. Ltd., SUS Environment, China Tianying Inc. and Sanfeng Environment. A total of 200 million data samples were employed for the quantification and classification of the operational level (Supplementary Method 1). Data on the feedstock entering incinerators, including MSW composition (332 samples), elemental content of each type of waste (204 samples), and type (285 samples) and consumption (148 samples) of the auxiliary fuel, were sampled for experimental analysis or collected from the literature (Supplementary Tables 4 and 5, Supplementary Figs. 7 and 8).

Electricity generation and self-consumed energy at the incinerator level were sourced through the on-site measurements, verified by provincial and municipal development and reform commissions, actual operational data from WTE plants, and annual reports of operating enterprises. Daily average emission concentrations of FGPs at the incinerator level, including PM, NOx, SO2, CO, and HCl, were obtained from the national continuous emission monitoring system, comprising a total of 602,254 data samples. Details of data preprocessing and calculation of the total emission of FGPs are provided in Supplementary Method 1 and Supplementary Methods 4 and 5. CO2 emissions from MSW incineration (from both biogenic and fossil carbon), CH4 and N2O emissions from waste decomposition and flue gas treatment processes (Supplementary Table 6), and CO2 emissions from auxiliary fuel combustion were considered in GHG emissions from WTE plants. The carbon content for each waste category in China was corrected (Supplementary Table 5), and the proportions of biogenic and fossil carbon in the total carbon content were set based on the Intergovernmental Panel on Climate Change Guidelines for National Greenhouse Gas Inventories (Supplementary Method 6). Waste generation and composition were significantly related to local demographic characteristics and economic levels28. Artificial neural network models were employed to predict the EEef, MSW generation, and composition till 2035. Missing data on waste composition and energy output were also filled using these prediction models (Supplementary Method 3 and Supplementary Figs. 9-10).

Energy efficiency evaluation
This study employed a weighted assessment of WTE plant energy recovery efficiency by applying distinct weighting factors to electricity and heat generation, reflecting their fundamentally different energy conversion pathways. The evaluation accounts for significant disparities in primary energy requirements, as evidenced by the 38% conversion efficiency of coal-fired power plants versus 91% efficiency in thermal heating systems26. To standardize efficiency metrics across varied energy outputs, we adopted sector-specific weighting coefficients derived from coal-based energy infrastructure. This approach avoids overestimating the energy efficiency of heat-only recovery systems and enables electricity and heat to be assessed on a unified basis of equivalent primary energy consumption, allowing more equitable cross-plant comparisons and supporting the fair benchmarking of WTE facilities under mixed energy recovery modes. The system boundary of EEef calculation was restricted to the WTE plants, including fermentation, incineration, thermal energy recovery, and FGPs control, excluding MSW collection, transportation, and the by-products treatment, such as fly ash and bottom ash.

EEef was calculated by Eq. (1) as below.

EEef = Ep / (Ef + Ei) / 0.97 * (Ew + Ef) (1)

where Ep represents energy produced as heat or electricity including energy exported to external system and self-consumed in the process of MSW disposal, which is weighted by the equivalent factors of electricity and heat; Ef is fossil fuel energy input to the thermal system contributing to the combustion process; Ew is the energy contained in the waste fed into furnace; Ei is other energy imported excluding Ef and Ew with steam production. Usually, energy conversion rate relies greatly on the energy types, and approximately 2.6 MWh of primary fuel is required per one MWh of electricity (MWhe), while only 1.1 MWh of primary fuel is consumed for one MWh of heat (MWhh) in dedicated power plants63. To normalize the energy recovery rate, the energy efficiency formula incorporates the corresponding equivalence factors.

Ew (Energy input from MSW). Energy contained in MSW were evaluated by Eqs. (2–4). Multiple linear regression was employed to construct a quantitative relationship between waste components and LHV. R2-ajusted is 0.89, and p value is below 0.001.

Ew = MMSW * LHVMSW (2)

MMSW = Bi * Di (3)

LHVMSW = 22131.88 + 9.06Pplastic + 130.32Pwood - 44.93Pfood - 75.09Ptextile - 47.29Ppaper - 346.77Pglass - 196.52Pmetals - 65.74Pash - 52.81Pothers - 210.27Pmoisture (4)

where M is the mass of incinerated waste. B is the treatment capacity of the boiler i, t d-1, and D is the operational days per year, d. LHV represents LHV of MSW, kJ kg-1; Pplastic, Pwood, Pfood, Ptextile, Ppaper, Pglass, Pmetals, Pash, Pothers, Pmoisture represent the percentile of plastic and rubber, wood and bamboo, food waste, textile, paper, glass, metals, ash, others, and moisture in the MSW, wt.%.

Ef (Energy input from auxiliary fuel). With the increasing prevalence of non-coal firing technologies, it was assumed that all plants equipped with circulating fluidized bed adopt non-coal firing. The diesel consumption was calculated based on the diesel usage of grate furnaces of the same furnace size (Supplementary Fig. 7). Natural gas, as an auxiliary fuel, exhibits regional characteristics and requirements for the natural gas pipeline network. Excluding the currently known 31 incinerators using natural gas for auxiliary firing, it was assumed that the remaining grate furnaces utilize diesel.

Ep (Energy generation output to the external energy system and utilized with WTE plants). The data on energy output were collected from a publicly published dataset of the local governments. Electricity and heat consumed in WTE plants during FGPs control and other operational processes were calculated as follows.

Electricity self-used refer to the portion of electricity generated by waste incineration power plants that is consumed internally rather than exported to the external power grid. It is mainly used to support on-site operations, including waste feeding and handling systems, grate and furnace operation, flue gas treatment units, induced draft fans, cooling and circulating pumps, ash handling systems, auxiliary boilers, control systems, and plant lighting. Electricity self-use is an essential component of plant operation and directly affects net electricity output and overall energy efficiency, which is calculated by Eq. (5).

Eele-self = Eele-pro * Peri (5)

where Eele-self is self-used electricity, kWh; Eele-pro is the electricity production from waste incineration, kWh; Peri is the percentage of self-used electricity for the combination i of FGPs control process, %, which is shown in Supplementary Fig. 2.

Heat self-used denotes the fraction of thermal energy recovered from waste incineration that is utilized within the plant itself. This heat is primarily used for maintaining stable combustion conditions, preheating combustion air, drying incoming waste, start-up and shutdown processes, and providing space heating or hot water for on-site facilities. In some cases, self-generated heat also supports auxiliary processes such as leachate treatment and equipment frost protection. Heat self-use reduces the amount of recoverable energy available for external supply, but is necessary to ensure safe, stable, and efficient plant operation, which is evaluated by Eqs. (6–9).

Eheat-self = Vfg * HDi (6)

Vfg = (Bf * 10^6 / 3.6) * (LHVMSW + 0.77 + 1.0161 * (α-1) * V0) / Vw (7)

V0 = 10,000 / LHVMSW (8)

Bf = (0.111PH + 0.0124Pmoisture + 0.0161 * (α-1) * V0) / 3.6 (9)

where Eheat-self is self-used heat, kJ; HDi is the heat demand in combination i of different FGPs control devices, W Nm-3, which is presented in Supplementary Fig. 8; Vfg, V0, and Vw represent the volumetric flow rate of dry flue gas (m3 s-1), theoretical air requirement per unit mass of MSW (m3 kg-1), and the volume of water vapor in the flue gas (m3 s-1), respectively. B, α, and q respectively signify the boiler fuel consumption rate (t h-1), excess air coefficient, and thermal losses due to mechanical incomplete combustion in the furnace. PH represent the content of the element of hydrogen in MSW fed into the furnace, respectively, %.

Energy efficiency correction
The impact of climate on EEef primarily involves external temperature and heating demand. Variations in external environmental temperature can affect the efficiency of primary air, secondary air heating, and turbine work. Heating demand also has a significant impact on EEef. In northern regions where residents require heating during the winter, there are more opportunities to apply incineration technology combined with heat and power, which exhibits higher EEef. However, for incineration plants in China, dense population and urgent disposal needs necessitate the selection of remote, sparsely populated areas for construction and operation. Even in northern regions, the distance to heating demand points is considerable, resulting in the majority of WTE plants in China currently being electricity-only, with few examples of heat utilization.

The function of incineration plants has been shifted from the MSW disposal to the energy supplement facilities, and EEef is the key factor to evaluate the energy recovery performance. To mitigate the impact of uncontrollable factors on the evaluation of WTE plant efficiencies, such as climate conditions, geographical variations, and technological choices, and to place all plants on an equitable comparative basis, we developed a multidimensional efficiency correction factor shown in Eqs. (10–11) as follows.

EEef-adjusted = CF * EEef (10)

CF = CCFi,j * SCFi * OCFj (11)

where EEef-adjusted represents the adjusted EEef; CF is the correction factors of the incinerators installed in climate zone i with different heat demand (Supplementary Fig. 4), with treatment scale j; CCF is the climate correction factors; SCF is the correction factors of incinerators size; and OCF is the correction of incinerators located in places with lower oxygen partial pressure.

This factor integrates influences from heating demands associated with the annual number of heating days in China’s nine climate zones, oxygen availability affecting combustion conditions in plateau areas, and the long-term impact of technology types applied in the existing facility on energy recovery (Supplementary Method 2). While these classifications capture the major passive influences, such as zonal boundaries and size ranges, they inevitably simplify more nuanced regional and technical differences, and thus the adjusted factor retains some unavoidable limitations.

Scenario analysis
We constructed two primary categories of scenarios, BAU and ERE scenarios, aligned with the five SSP development pathways. For the BAU scenario, we developed a provincial-level neural network prediction model to estimate waste generation and composition characteristics from 2023 to 2035, incorporating demographic, economic, and social factors from SSP1-5. According to China’s 14th Five-Year Plan for Urban Household Garbage Classification and Treatment Facilities Development64, cities generating more than 300 tons of MSW per day without WTE treatment are mandated to accelerate the construction of these plants. These cities were identified as priority locations for new WTE infrastructure. We cross-validated the projected treatment capacity and geographical distribution of new plants with published development plans from 14 provinces and municipalities. Subsequently, MSW incineration quantities, electricity generation, and GHG emissions under the BAU scenarios were projected, considering variations in MSW generation, EEef and waste composition (Fig. 5). EEef was estimated through predictive models developed in this study, integrating factors such as local MSW LHV, operational experience, and technological characteristics (see Supplementary Method 3 for model details, Supplementary Figs. 10 and 11 for regression performance of models’ testing set, and Supplementary Figs. 12–27 for provincial MSW generation and composition data from 2023 to 2035). Emission factors of flue gas pollutants in BAU scenarios were forecasted by the prediction models37.

In the ERE scenarios, optimization strategies encompassed three categories: technological retrofits to address MSW with LHV exceeding 10,000 kJ/kg, operational-level improvements at outdated plants, and upgrades to CHP systems. Technological retrofits were primarily based on the practical experiences from Shanghai’s WTE facilities dealing with elevated LHV post waste separation from 2019. Specific retrofit actions included replacing air-cooled refractory furnace walls with water-cooled designs, adding heat-transfer surfaces in empty flue gas ducts to expand heat exchange area and lower flue gas temperatures, and raising steam temperature and pressure parameters. Operational experience enhancements mainly include the introduction of AI-based management systems and improvement of operator skill levels. Currently, most Chinese WTE plants solely produce electricity, with very few operating as CHP facilities. To improve overall energy utilization efficiency, CHP upgrades targeted a broader range of WTE plants, providing district heating in colder regions and supplying industrial steam in economically developed warm regions.

The costs for equipment procurement, increased operational maintenance expenditures, and construction durations associated with each measure contained in ERE scenarios were detailed in Supplementary Table 2. Considering varying economic capacities among different cities, we tailored differentiated EEef enhancement strategies for WTE plants according to distinct economic development levels under the five SSP scenarios; the specific parameter settings are shown in Supplementary Table 7. Finally, we explored the energy substitution potential and associated emissions of GHG and FGPs.

Uncertainties analysis
The Monte Carlo simulation was employed to estimate the uncertainties of energy generation and GHG emissions in 2023, and 10,000 estimations were performed. Normal distribution with a variation within 10% was assumed for all the data of waste composition, WTE capacity for each incinerator, annual operational days, usage of auxiliary fuel, self-used electricity and heat energy and energy output data. The uncertainties of emission factors of NOx and CH4 during flue gas treatment process were collected from literature review (Supplementary Table 6), and their statistical distribution was set as normal distribution.

Bootstrap and Monte Carlo methods were used to evaluate the 95% confidence intervals of FGPs’ emission factors and emission inventory. We preprocessed the emission data collected from the monitoring systems, and the detailed process was depicted in Supplementary Method 7. After preprocessing, the data probability distribution of each FGP was estimated based on the monitoring data of different incinerator types and regions in 2023. For WTE plants without monitoring data, a bootstrap statistical method was used to estimate their emission factors based on the emission data from the same incinerator type and region, after 10,000 iterations. For the data collected from the monitoring systems, detected device errors and missing data errors were considered, as described in detail in Supplementary Table 8. The coefficient of variation values of activity data, flue gas volume, and annual operational days were set to 10%, and their statistical distribution was set as the normal distribution65. After these processes, a Monte Carlo simulation was employed to estimate the 95% confidence intervals of emission factors and emission inventories, and 10,000 estimations were performed.

Monte Carlo methods were used to evaluate the 95% confidence intervals of energy recovery, GHG, and FGPs emissions in 2035. A normal distribution with a variation within 10% was assumed for the temperature in the chamber, single furnace processing capacity, operational experience, waste composition, total population, proportion of elderly residents, proportion of small households, birth rate, and illiteracy rate used in the prediction models of EEef, MSW generation, and MSW composition. A total of 10,000 Monte Carlo simulations were performed for these input variables, and 10,000 prediction iterations were conducted. The corresponding average values of energy recovery, GHG, and FGPs emissions in 2035 and their 95% confidence intervals were obtained.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Nature Communications Manuscript Review\nManuscript Title: 10-13% Substitution Potential of Residential Electricity Consumption: Waste-to-Energy Plays a New\nEcological Niche in the Energy System\nKey Results:\nThis manuscript analyzes waste-to-energy (WTE) facilities across China to demonstrate their capacity for replacing 10–13%\nof residential electricity consumption (REC) through optimized energy recovery operations by 2035. The evaluation of\nWaste-to-Energy (WTE) performance analyzed energy efficiencies (EEs) and greenhouse gas (GHG) emissions while\nassessing regional operational imbalances through a detailed database of 974 WTE plants and their 2,150 processing lines.\nThe authors propose a three-level energy recovery plan which focuses on equipment upgrades and operational\nenhancement alongside carbon capture system integration. WTE plants demonstrate their effectiveness at GHG emission\nreduction while simultaneously reducing energy poverty and advancing SDGs 7, 11 and 13.\nThe notable findings include:\n1. Electricity yield from WTE plants currently accounts for approximately 7% of REC in China, with the potential to reach 10–\n13% by 2035.\n2. Significant regional imbalances in energy efficiencies, with coastal regions lagging behind inland regions due to higher\nenergy losses and outdated technologies.\n3. WTE plants generate substantial GHG emissions (approximately 265–287 Mt CO2-eq annually), but carbon capture\ntechnologies could transform them into carbon-neutral or even carbon-negative facilities.\nValidity:\nThe data interpretation and conclusions are generally valid and robust, supported by a large operational database and\nrigorous analysis. The study uses a bottom-up approach, integrating plant-level operational data, regional energy demands,\nand climate zone corrections, which strengthens its reliability. However, the following concerns should be addressed:\n1. GHG Emissions Accounting: The manuscript does not provide sufficient detail on the methodology used to separate\nbiogenic and fossil-derived CO2 emissions (line 361). This distinction is critical for the validity of the carbon-neutrality\nprojections.\n2. Regional Factors: While the manuscript discusses regional imbalances in energy efficiency, it lacks an in-depth analysis\nof how socio-economic and policy factors influence these disparities.\n3. Assumptions in Projections: The projections for 2035 rely heavily on assumed technological and operational\nimprovements, which may not fully account for economic or regulatory constraints. This could affect the feasibility of\nachieving the suggested energy recovery rates.\nOverall, the manuscript is valid, but these gaps need to be addressed to improve the robustness of the conclusions.\nSignificance:\nThe manuscript has significant implications for the waste management and energy sectors, particularly in the context of de-\ncarbonization and the circular economy. The findings demonstrate:\n1. The critical role of WTE plants in addressing energy poverty and reducing reliance on fossil fuels, especially in regions\nwith growing waste generation and limited land for landfills.\n2. The potential for WTE plants to contribute to carbon-neutral energy systems through technological upgrades and carbon\ncapture.\nHowever, the authors should carefully check to ensure that the manuscript’s novelty is not compromised by existing literature\nthat has already highlighted the potential of WTE plants for energy recovery and GHG mitigation (e.g., Liu et al. 2024, Hoy et\nal. 2023). Although, this study’s focus on regional efficiency disparities and tiered strategies clearly adds value and but does\nnot entirely set it apart from previous studies.\nData and Methodology:\nThe methodology is sound and well-documented, with a robust framework for assessing energy recovery and GHG\nemissions. Key strengths include:\n1. The use of a detailed operational database covering 974 WTE plants and 2,150 processing lines, which ensures a high\nlevel of granularity.\n2. The integration of regional and climate zone-specific correction factors for energy efficiency evaluation.\n3. A tiered optimization strategy that provides actionable recommendations for improving energy recovery.\nHowever, the following issues should be addressed:\n1. The methodology for calculating energy efficiencies (EEef) is complex and may not be easily reproducible. Simplifying the\npresentation or providing supplementary materials could improve clarity.\n2. Limited data on the composition of municipal solid waste (MSW) across regions reduces the ability to generalize the\nfindings. Including more detailed data on MSW characteristics would strengthen the analysis.\nAnalytical Approach:\nThe analytical approach is strong, with a combination of statistical modeling, scenario analysis, and regional-level\nassessments. The use of equivalence factors (EEef) and correction matrices is innovative and provides valuable insights.\nKey strengths include:\n1. The consideration of multiple factors affecting energy recovery, such as MSW composition, incinerator size, and\noperational experience.\n2. The use of Shared Socioeconomic Pathways (SSPs) to project energy recovery and GHG emissions, which aligns with\nglobal climate modeling frameworks.\nHowever, the manuscript could benefit from:\n1. A clearer explanation of how uncertainties in the projected scenarios were addressed. While Monte Carlo simulations\nwere employed, the assumptions and confidence intervals need more transparency.\n2. A more detailed discussion of the limitations of the analytical approach, particularly regarding the scalability of the\nproposed strategies to other low- and middle-income countries.\nSuggested Improvements:\n1. GHG Emissions Accounting: Provide more detail on the methodology used to distinguish biogenic and fossil-derived CO2\nemissions. This is critical for validating the carbon-neutrality claims.\n2. Regional Analysis: Include a more detailed discussion of the socio-economic and policy factors driving regional\ndisparities in WTE performance.\n3. Waste Composition Data: Incorporate more detailed data on MSW composition across regions to strengthen the analysis\nof energy recovery potential.\n4. Clarity in Methodology: Simplify the description of the EEef calculation process or provide supplementary materials to\nimprove accessibility.\n5. Projections Feasibility: Address the economic and regulatory constraints that may affect the feasibility of achieving the\nprojected energy recovery rates by 2035.\nClarity and Context:\nThe manuscript is generally clear and well-structured, with a logical flow of ideas. The introduction provides sufficient\ncontext, and the results are presented in detail. However, the manuscript could benefit from:\n1. Improved Accessibility: The technical language and complex methodologies may be difficult for non-specialist readers to\nfollow. Simplifying key sections or including a glossary of terms could improve accessibility.\n2. Contextualization of Findings: While the manuscript references global trends in WTE, it lacks a thorough comparison with\nsimilar studies in other countries or regions. Including such comparisons would enhance the contextualization of the\nfindings.\nConclusion\nThis manuscript provides a valuable contribution to the field of waste-to-energy and sustainable energy systems, particularly\nthrough its detailed analysis of regional disparities and tiered strategies for energy recovery enhancement. However,\naddressing the highlighted gaps—especially in GHG emissions accounting, regional analysis, and methodology clarity—\nwould significantly strengthen the work. If these concerns are adequately addressed, the manuscript has strong potential for\npublication and could serve as a key reference for WTE development in both high-income and low- and middle-income\ncountries.\nReferences\nThe references provided in this manuscript are relevant and mostly from recent journal publications covering a period from\n2020 – 2025.\nAdditional comments:\nLines 47 -48: Revise the sentence “Energy recovery potential is supposed to…” there seems to be a missing word here.\nLine 91: change the word “difference” to different", "reviewer_2": "The authors have made a commendable effort in compiling a comprehensive dataset and conducting detailed analyses.\nHowever, the manuscript would benefit from a clearer articulation of its novel contributions and stronger positioning of its\nrelevance beyond the Chinese context. Some structural and clarity issues also limit the overall impact. Given these\nconcerns, I am not able to recommend the manuscript for publication in its current form.\nHigh-level comments\n1.Generalizability and Use of Region-Specific Terminology\nThe title suggests a broad relevance to the WtE industry, yet the analysis is focused exclusively on China's WtE\ninfrastructure and simulation results. If the title remains, the manuscript does not sufficiently address how the findings could\nbe extrapolated or adapted to other regions with different waste compositions, energy conversion systems, or policy\ncontexts. On top of the generalization issue, some terms used in the manuscript are specific to China, which may hinder\ncomprehension for readers in other countries. To enhance the manuscript’s global relevance and applicability, the authors\nshould either explain such terms more clearly or provide internationally comparable alternatives and draw parallels where\nappropriate.\n2.Commendable Effort, but Clearer Articulation of Novelty is Needed\nThe authors have clearly devoted significant time and effort to compiling a comprehensive database and conducting\nscenario-based simulations. This type of work is often labor-intensive and logistically challenging. The authors' dedication is\ncommendable. However, the manuscript currently reads more like a detailed report of a case study than a research article\nintended for a top-tier journal, with too many number reporting and not adequate discussions. Many of the findings also align\nwith prior literature, and the regional analysis, data scope, and policy relevance do not appear to provide novel insights (or\nnot clearly elaborated). One key concern is the logic leading to the newly proposed efficiency equivalence factor (EEef-\nadjusted)—lacks a clear and structured flow (see the detailed comments later). To enhance the impact of this study, the\nauthors need to explicitly highlight what is new and how their work advances the field. Whether it be through a more\ninnovative simulation approach, a clearer demonstration of policy relevance, or a stronger justification for the new factors\nproposed, clearer articulation of these contributions is essential for readers to appreciate the value of the study.\n3.Disconnected Presentation of Results\nDespite the substantial effort evident in database construction and simulation, the narrative and message delivery are\nsignificantly weakened by a lack of coherence between the text and the figures. The figures often contain a large amount of\ninformation that is not discussed in the text, making it difficult for readers to identify key trends or data points which can\neffectively link the figures to the text (see specific comments later). The manuscript would benefit from clearer guidance on\nhow to interpret the figures and how each figure/sub-figure supports the key arguments. A more structured and cohesive\npresentation, with better cross-referencing between the figures and the text, is essential to improve clarity and reader\nengagement.\n4. Structural and Communication Issues\nThe narrative does not effectively communicate the storyline or insights of the study. A more logically organized\npresentation, with stronger linkage between sections (especially the flow from the geographic differences to EF/EE results to\nthe GHG and SGD), would help readers grasp the relevance and significance of those findings. Attention to clarity, figure\ncaptions (this is important), and cross-referencing would substantially improve the manuscript’s accessibility.\nSection-specific comments\nIntroduction\n-Careful articulation is needed. In the third paragraph of the Introduction, the authors state that “MSW composition undergoes\nsubstantial changes, especially for the moisture content and the lower heating values (LHV), due to the economic\ndevelopment and waste management system reforms, such as enforced source separation…”. While the intention to\nhighlight the discrepancy between the original design parameters and the evolving characteristics of input materials is\nunderstandable and relevant, the supporting references—studies from Thailand (2021) and India (2017)—might not be the\nbest to support this claim. In addition, the variation in MSW composition is acknowledged as a key issue, but the authors\ndo not sufficiently explain how this study addresses or mitigates this challenge—particularly in settings beyond China. Or do the\nauthors try to address this issue? The introduction section did not lay a good foundation to develop the story further into the\nresults and discussion.\n-Language issue. In the 3rd paragraph of the introduction, the authors indicated that “To normalize the difference of the\nenergy utilization forms, EEs weighted by equivalence factors (EEef) should be employed22. “ This reads as if the use of\nEEef should be adapted in this study. It is only in the later section for it becomes clear that the authors want to propose an\n“adjusted” factor, EEef-adjusted. The author should pay more attention to the language delivery.\nMain Text\n1.Regional imbalance of WTE plants for electricity recovery and flue gas pollutants control\n-What is the role of flue gas pollutants (FGPs) in the matrix? FGP appears in the first section of the main text, indicating its\nimportance. However, it then disappears in the following discussion and matrix. This delivered a confusing message to the\nreaders.\n-Text vs Fig 1. Figure 1 looks great, but it did not help to deliver a good message in the main text.\no When the authors refer to Fig 1 saying that the coastal regions NC/EC/SC are significant contributors, the notation of\nregions in Fig 1 (EC/NC/MYR..) do not echo the text. I cannot find “SC” in Fig 1, but get confused by MRY/MRYR notations.\no The authors try to pull up the comparison of coastal vs in-land regions in this section. However, the notation of regions\n(EC/NE/MRY/MRYR/NC) are nowhere to be found in the map, making it very difficult for the readers outside of China to\ncomprehend.\no Fig 1b-f, the color choices are bad, making the bar charts not readable. The figures contain lots of information, but the\nauthors did not guide the readers properly. For example, which regions are coastal and which are in-land? More importantly,\nif the authors do not intend to discuss the density distribution of EFfgp and electricity in details, you do not need to show\nthem in the figure.\n- What is the main message of this section? We see lots of numbers reported here but no clear delivery on the message. For\nexamples, in the opening sentence of 2nd paragraph, the authors indicated 5 FGPs, each with a total emission and an\nemission factor, in one single sentence. After trying hard to match those values, I then realized that this piece of information\nwas not used or even mentioned in the following EF or EE discussions. This is a potential paper in the top journal, not a\ncase study report. The authors should pay more attention to the story flow and focus on the key findings to support the story.\n- The authors indicated that “since both operational experience and incinerator enlargement showed positive impacts on\npollution control and energy recovery (2nd paragraph of this section)”. This is confusing as people in general will not\nconsider the “operational experience a direct factor. It is NOT until the next section (Fig 2) for the term “work experience” to\nshow up. This makes the reading flow difficult.\n2.Tiered criteria of EEef and impact factors\n- The opening sentence indicates that EEef in China (0.54) and EU(0.55) are comparable. Choosing a paper from 2012 with\nthe data from 2007-2010 in the EU is a bad choice. Especially in the introduction section that the author highlights the rapid\nchange in WtE, the opening statement like this instantly compromises the trust established in the previous sections.\n- Again, the figure-text alignment and visual organization requires further arrangement. In the 1st paragraph of this section. In\nthe first paragraph of this section, the authors refer to Fig. 2 to discuss WtE capacity, EEef values, and steam parameters.\nWhile the intention is appreciated, the linkage between the text and the figure is not intuitive. From the reader’s\nperspective, it is difficult to extract the relevant information from Fig. 2 based on the current narrative. The violin plots on the\nright-hand side—presumably containing the key comparative data—occupy only about 25% of the figure space, and the\ndiscussion in the text focuses mainly on mean values. This raises the question: why present such a large, complex figure if\nonly a narrow subset of the information is actually discussed?\n- Additionally, the layout of Fig. 2 could be significantly improved for readability and logical flow. If the intention is to analyze\nand compare capacity, steam parameters, and flue gas pollutant (FGP) levels together, these elements should be grouped\nadjacently near the top of the figure. In contrast, the \"experience level\" section—which seems less central to the discussion\n—could be moved to the bottom or repositioned elsewhere. The current arrangement makes it difficult to match specific parts\nof the figure with corresponding parts of the text, reducing the overall effectiveness of both.\n- I can not see the logic flow from EEef to EEef-adjusted. The authors spend a good amount of effort to elaborate the\nrelationship between EEef and incinerator scale, steam parameter, and FGP control, followed by one paragraph to highlight\nthe importance of operation experience level. However, in the next paragraph on EEef-adjusted factor, the matrix includes\nthe local heating demand, oxygen partial pressure, and incinerator size, which are very different from the parameters\nmentioned above. This again, from the message delivery perspective, diminishes the readability and importance of the\nproposed factor.\n- The authors uses AI models to simulate WTEs’ EEef in China via various parameters without applying energy input and\noutput data. This is not a problem by itself. However, readers might wonder if those results can be used to compare with the\nWTEs’ EEef in EU, where the EEef is calculated by specific equations by applying energy input and output data? Further\nvalidation or some justifications can help to generalize the findings and their applicability outside of China.\n3. Energy recovery potential and the substitution for REC in 2035\n- In this section, the authors provide the data from simulations with lots of numbers. While results are important, the take-\nhome messages and the in-depth discussions are not there. For example, Fig 4 shows 7 scenarios but only three are\nmentioned in the text for number reporting, leaving the readers puzzled on the data interpretation and potential applications.\n4. Co-benefits of GHG and carbon negative potential.\n- Subtitle might be misleading.\nThis study includes the bio-GHG, the net GHG in this study is calculated by:\nnet GHG = bio GHG + fossil GHG - avoided GHG from energy recovery\nWith this approach, the net GHG in this study is currently positive, meaning that the WtE’s emission factor is higher than\nChina’s energy mix. It might not be appropriate to say GHG-benefits for now, but maybe in the future, with the development\nof the CCUS and energy efficiency improvement. This is a wording issue.\n- From the opening sentence of this section, I can understand that the authors try to highlight the importance of GHG from\nWtE. While efficiency improvement is important, the way to communicate the message should be further improved. Do note\nthat GHG from WtE is ~80 MtCo2-eq/yr and the total GHG emission from China is ~13Billiont-CO2-eq/yr. Starting the\nparagraph with “Huge amounts of GHG were released from WTE plants” might help to attract the attention from the readers,\nbut the authors should be careful and link it to the overall picture relative to the whole GHG picture if GHG is the pitching\npoint.", "reviewer_3": "The manuscript submitted to Nature Communications, titled \"10-13% substitution potential of residential electricity\nconsumption: waste-to-energy plays a new ecological niche in the energy system,\" provides an analysis of waste-to-energy\n(WTE) incineration in China, focusing on its role in waste management, energy production, and climate mitigation\ndespite existing efficiency challenges. Using data from 974 plants, the study evaluates energy recovery, emissions, and regional\ndisparities. The study suggests that WTE could supply 7% of residential electricity, with an average generation of 361\nkWh/ton, though it emits 265–287 million tons of CO₂ annually. By 2035, optimized strategies could increase generation to\n197–259 TWh, replacing 10–13% of local electricity. While the manuscript covers promising areas, major revisions are\nneeded to address the following concerns:\n1. The English should be revised by native-speaking scientists, and it’s important to consistently use either \"ton\" or \"tonne\"\nthroughout the manuscript.\n2. The abstract needs revision. For example, lines 32-34 are unclear. Please clarify the three-tiered scenarios mentioned\nand their corresponding impacts/results. Additionally, specify the expected CO₂ reduction for each scenario, or on average,\nto highlight the significance of the study. The phrase \"Energy recovery potential is supposed to 197–259 TWh by 2035\"\nshould be rephrased for clarity, i.e., does \"supposed to\" mean \"expected to reach\"? The term \"awkward position\" is informal\nand unclear in this context.\n3. The manuscript uses numerous abbreviations; it would be helpful to reduce their use and include a list of abbreviations for\nclarity.\n4. Use an en dash (–) between numbers throughout the manuscript.\n5. The introduction should be rewritten to emphasize the potential of incineration to meet energy needs in select countries,\nparticularly in China, and to highlight the research gap that this study addresses.\n- The manuscript mentions that WTE has \"become the dominant technology\" in some regions (Line 60) but does not provide\nsupporting data or references. Adding relevant statistics would strengthen this statement.\n- The claim that WTE \"could provide the localized solutions for both sides, line 74\" to both energy shortages and waste\ndisposal needs more justification. Are there case studies or pilot projects demonstrating this potential?\n- The authors mentioned that moisture content and LHV affect EE, but specific examples are not provided. A brief\ncomparison of MSW compositions across regions, such as high moisture content in China versus moisture content in\nEurope, would add context.\n6. Lines 130-131 mention \"regional imbalance of WTE plants for electricity recovery and flue gas pollutant control.\" Please\nclarify whether this refers to China or worldwide.\n7. For figures (e.g., Fig. 1, Fig. 2, Fig. 4), the relationships between variables could be clearer. The figure captions should be\nmore descriptive, particularly for readers unfamiliar with the technical aspects of WTE systems. For example, Fig. 4 needs\nclarification in line 278.\n8. The manuscript suggests that regional differences in incinerator size and operational experience impact emissions. It is\nbetter to explain the exact mechanisms driving these disparities.\n9. A more thorough discussion of the technological advancements needed to achieve the projected increases in energy\nrecovery by 2035 would strengthen the paper. For example, what specific technological improvements could facilitate the\nshift from \"MSW disposal\" to \"energy recovery\" plants?\n10. The economic and technological feasibility of large-scale carbon capture implementation deserves more attention. An in-\ndepth analysis of how realistic these projections are would provide valuable insight.\n11. Please clarify how uncertainties in the emissions data (e.g., Table S2) are accounted for. Are these uncertainties due to\nmeasurement errors, regional differences, or other factors?\n12. The paper should illustrate how waste composition differs across regions and how these differences impact energy\nrecovery and emissions.\n13. The feasibility of carbon capture technology should be expanded upon to provide a more comprehensive view of its\npotential in WTE systems.\n14. The first two references in lines 343-347 need links, as do references 25-30, and similar ones. References in Chinese\nshould be also linked.\n15. The manuscript should discuss how the results from this study could benefit other nations, particularly developing\ncountries.\n16. Can you briefly compare the incineration process for WTE with other thermal processes, such as gasification, using\nrelevant statistics?\n17. Please add conclusion to your findings"}, "author_response": "Dear Reviewers,\nWe sincerely appreciate your thorough review of our manuscript entitled “10–13%\nsubstitution potential of residential electricity consumption: Waste-to-energy plays a new\necological niche in the energy system” (NCOMMS-25-05507-T). Your insightful and\nconstructive feedback has been extremely helpful in refining the structure, clarifying the\nlogic, and further articulating the novelty and contributions of this study. Each point has\nbeen revised carefully as best as we can.\nIn direct response to your insightful comments, we undertook an extensive, nearly\nthree-month revision process. This was not merely an editorial exercise; it involved a deep\nre-evaluation and enhancement of our work. Crucially, to address specific points regarding\nenergy efficiency and operational realities, we conducted new field research. This included\na significant visit to the Lhasa waste-to-energy (WTE) plant in the Qinghai–Tibet Plateau\non July 28, 2025, where we gathered first-hand data on their pioneering implementation of\nlow-oxygen content combustion technology – a key factor influencing energy efficiency\nrelevant to our analysis. This empirical work directly informed and strengthened our\nrevisions.\nActually, we have performed a comprehensive review and critical analysis of recent\nhigh-quality publications in this field. These studies have predominantly focused on the role\nof WTE plants in greenhouse gas (GHG) mitigation within the solid waste sector, especially\nregarding landfill methane reduction. While they only address the necessary first step in\ntransitioning from landfilling to incineration. Our research sought to answer a different and\npressing question: in the current context of the rapid expansion of WTE facilities, how\nshould their functional role be classified, particularly given the ongoing debate over the\nlegitimacy of their role as power plants when energy efficiency is low? We evaluated\ncurrent energy efficiency, their influencing factors, and the optimization potential, based on\na first-hand dataset of 975 WTE plants across China. We further conducted a cost–benefit\nassessment of 11 targeted improvement measures and develop a correction factor matrix\nto ensure that adjusted energy efficiency (EE ) accounts for and eliminates the\nef-adjusted\ninfluence of uncontrollable external factors. The EE metric can be applied broadly\nef-adjusted\nto large-scale WTE facility evaluations, enabling equitable benchmarking across regions\nand technologies.\nWith the guidance of your constructive comments, we have thoroughly revised the\nmanuscript to improve its clarity, robustness, and scientific contribution. Major structural\nadjustments include, but are not limited to:\n• Merging flue gas pollutants (FGPs) and GHG discussions into a co-benefits\nframework analyzing how energy efficiency improvements can simultaneously\nreduce pollution and greenhouse effect;\n• Restructuring the storyline to explicitly highlight the novelty of the study and clearly\ndifferentiate it from previously published literature;\n• Systematically revising the text to strengthen the policy relevance, analytical depth,\nand implications of the findings.\nIn addition to the above revisions regarding our novelty and contribution, we have\ncarefully addressed all other concerns. These include, but are not limited to:\n• Improved Data Presentation and Interpretation: We enriched the data analysis\nsections with clearer statistical explanations, contextualized regional differences\n(e.g., waste composition, climate, and plant scale), and provided expanded\ndiscussion to support key findings.\n• Enhanced Figure and Table Clarity: All figures (e.g., Figures 1, 2, 4, and 5) have\nbeen revised for better clarity and coherence. We rewrote figure captions to be\nmore descriptive, especially for readers unfamiliar with the technicalities of WTE\nsystems. The visual consistency between figures and related text has also been\nimproved, with clearer variable labeling, units, and policy scenario annotations.\n• Expanded Scenario Description and Policy Relevance: The descriptions of\nfuture scenarios have been rewritten to provide clearer assumptions, boundary\nconditions, and implementation feasibility. We now explicitly outline three major\ncategories of 11 efficiency improvement measures, their cost-benefit\ncharacteristics, and the policy instruments that could support their adoption—\nenhancing both realism and relevance.\n• Discussion of International Applicability: We now include a dedicated section\nin the Conclusion and Discussion that reflects on how the study’s findings can\ninform WTE transitions in other emerging economies, accounting for technical,\nfinancial, and institutional barriers.\n• Expanded Uncertainty Analysis: We supplemented our uncertainty assessment\nfor the emissions inventory, clarifying the sources of uncertainty (e.g.,\nmeasurement error, regional parameter variability) and detailing how they were\naccounted for in the results.\nAll responses to your comments are detailed below, and all revised text from the\nmanuscript cited in our responses has been highlighted in light blue. We have also included\na table of contents to help locate each reviewer’s comment and our corresponding\nresponse. Once again, we greatly appreciate your valuable suggestions, which have\nsubstantially improved the quality and clarity of our work.\nBest regards,\nLou Ziyang on behalf of all authors"}, {"round": 2, "reviewer_comments": {"reviewer_3": "The revisions adequately address my concerns, and the manuscript is now suitable for publication.", "reviewer_4": "First, I would like to thank you for the opportunity to review this paper.\nThe aim of the paper is to assess the potential for substituting residential electricity consumption in China through waste-to-\nenergy (WTE) processes. As the title suggests, the authors estimate a substitution potential of 10–13% based on their\nresults. The methodology is robust and comprehensive, utilizing a database of 975 WTE plants and 2,151 incineration lines.\nIt accounts for the geographic location of each facility and includes several municipal solid waste (MSW) samples. This\ndatabase could be highly valuable for future research. Furthermore, the authors classify the plants into three categories:\ndisposal-oriented, energy recovery, and green energy plants. They also analyse 11 optimization strategies.\nGiven the robustness of the methodology, the high-quality data provided, and the relevance of the findings for a country like\nChina, I believe this paper merits consideration for publication. However, I recommend a few minor revisions.\nIn the introduction, the authors focus on the energy potential of MSW, which aligns with the paper’s goal. However, they do\nnot mention other significant uses of MSW, such as compost production or the recovery of critical raw materials. These\nopportunities are lost during incineration. It should be acknowledged that WTE is not the ideal solution to the MSW problem.\nComposting and material recovery are preferable alternatives. This point is briefly recognized by the authors in lines 68–69,\nwhere they note the low energy efficiency of WTE technologies, implying that many resources are lost during incineration.\nNonetheless, WTE remains a rapid and practical solution for densely populated areas and is preferable to landfilling.\nRegarding the main text of the paper some clarifications are required:\nRegional Imbalance: The authors analyse the uneven distribution of energy recovery from WTE plants across China. In lines\n156–157, they claim that the lower heating value (LHV) of MSW correlates with economic growth. This relationship should\nbe explained more clearly, as it is currently ambiguous. Also, the meaning and calculation of the EEef indicator are unclear.\nThe authors should clarify how it is derived, how it differs from standard energy efficiency metrics, and why it is used.\nEEef-Adjusted Factor: The authors introduce an EEef-adjusted factor to compare facilities while controlling for passive\ninfluencing variables. However, I think that the methodology behind this adjustment should be better explained in the main\ntext, including any limitations.\nProjection for 2035: The paper estimates energy recovery and substitution potential for residential electricity in China in\n2035. While the LHV of MSW is projected to increase, the authors should explain the factors driving this trend. Additionally, it\nis unclear whether the projections account for the expansion or construction of new WTE plants to accommodate rising\nMSW volumes.\nThe conclusion and discussion sections are lengthy and should be split into two distinct parts: one for discussion and one\nfor conclusions. It is also important to reiterate that WTE is not a unique solution to the MSW challenge. MSW is a complex\nwaste stream that can also yield valuable sustainability resources, such as compost for soil improvement and critical raw\nmaterials for industrial use."}, "author_response": "Dear Reviewers,\nWe sincerely appreciate your thorough review of our manuscript entitled “10–13%\nsubstitution potential of residential electricity consumption: Waste-to-energy plays a new\necological niche in the energy system” (NCOMMS-25-05507-T). Your insightful and\nconstructive feedback has been extremely helpful in refining the structure, clarifying the\nlogic, and further articulating the novelty and contributions of this study. Each point has\nbeen revised carefully as best as we can.\nIn direct response to your insightful comments, we undertook an extensive, nearly\nthree-month revision process. This was not merely an editorial exercise; it involved a deep\nre-evaluation and enhancement of our work. Crucially, to address specific points regarding\nenergy efficiency and operational realities, we conducted new field research. This included\na significant visit to the Lhasa waste-to-energy (WTE) plant in the Qinghai–Tibet Plateau\non July 28, 2025, where we gathered first-hand data on their pioneering implementation of\nlow-oxygen content combustion technology – a key factor influencing energy efficiency\nrelevant to our analysis. This empirical work directly informed and strengthened our\nrevisions.\nActually, we have performed a comprehensive review and critical analysis of recent\nhigh-quality publications in this field. These studies have predominantly focused on the role\nof WTE plants in greenhouse gas (GHG) mitigation within the solid waste sector, especially\nregarding landfill methane reduction. While they only address the necessary first step in\ntransitioning from landfilling to incineration. Our research sought to answer a different and\npressing question: in the current context of the rapid expansion of WTE facilities, how\nshould their functional role be classified, particularly given the ongoing debate over the\nlegitimacy of their role as power plants when energy efficiency is low? We evaluated\ncurrent energy efficiency, their influencing factors, and the optimization potential, based on\na first-hand dataset of 975 WTE plants across China. We further conducted a cost–benefit\nassessment of 11 targeted improvement measures and develop a correction factor matrix\nto ensure that adjusted energy efficiency (EE ) accounts for and eliminates the\nef-adjusted\ninfluence of uncontrollable external factors. The EE metric can be applied broadly\nef-adjusted\nto large-scale WTE facility evaluations, enabling equitable benchmarking across regions\nand technologies.\nWith the guidance of your constructive comments, we have thoroughly revised the\nmanuscript to improve its clarity, robustness, and scientific contribution. Major structural\nadjustments include, but are not limited to:\n• Merging flue gas pollutants (FGPs) and GHG discussions into a co-benefits\nframework analyzing how energy efficiency improvements can simultaneously\nreduce pollution and greenhouse effect;\n• Restructuring the storyline to explicitly highlight the novelty of the study and clearly\ndifferentiate it from previously published literature;\n• Systematically revising the text to strengthen the policy relevance, analytical depth,\nand implications of the findings.\nIn addition to the above revisions regarding our novelty and contribution, we have\ncarefully addressed all other concerns. These include, but are not limited to:\n• Improved Data Presentation and Interpretation: We enriched the data analysis\nsections with clearer statistical explanations, contextualized regional differences\n(e.g., waste composition, climate, and plant scale), and provided expanded\ndiscussion to support key findings.\n• Enhanced Figure and Table Clarity: All figures (e.g., Figures 1, 2, 4, and 5) have\nbeen revised for better clarity and coherence. We rewrote figure captions to be\nmore descriptive, especially for readers unfamiliar with the technicalities of WTE\nsystems. The visual consistency between figures and related text has also been\nimproved, with clearer variable labeling, units, and policy scenario annotations.\n• Expanded Scenario Description and Policy Relevance: The descriptions of\nfuture scenarios have been rewritten to provide clearer assumptions, boundary\nconditions, and implementation feasibility. We now explicitly outline three major\ncategories of 11 efficiency improvement measures, their cost-benefit\ncharacteristics, and the policy instruments that could support their adoption—\nenhancing both realism and relevance.\n• Discussion of International Applicability: We now include a dedicated section\nin the Conclusion and Discussion that reflects on how the study’s findings can\ninform WTE transitions in other emerging economies, accounting for technical,\nfinancial, and institutional barriers.\n• Expanded Uncertainty Analysis: We supplemented our uncertainty assessment\nfor the emissions inventory, clarifying the sources of uncertainty (e.g.,\nmeasurement error, regional parameter variability) and detailing how they were\naccounted for in the results.\nAll responses to your comments are detailed below, and all revised text from the\nmanuscript cited in our responses has been highlighted in light blue. We have also included\na table of contents to help locate each reviewer’s comment and our corresponding\nresponse. Once again, we greatly appreciate your valuable suggestions, which have\nsubstantially improved the quality and clarity of our work.\nBest regards,\nLou Ziyang on behalf of all authors"}, {"round": 3, "reviewer_comments": {"reviewer_4": "The authors have satisfactorily addressed all comments from the previous review. In my opinion, this paper can be\npublished in its current form."}, "author_response": ""}]

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