Climate change is a major challenge facing the world today, and it also provides a significant impetus for the transformation of energy systems and new economic growth. Against this backdrop, on September 22, 2020, during the general debate of the 75th United Nations General Assembly, China committed to the international community to enhance its nationally determined contributions to emissions reduction, aiming to peak carbon emissions before 2030 and striving for carbon neutrality by 2060. At the subsequent Climate Ambition Summit on December 12, President Xi Jinping further reaffirmed China's determination to achieve this goal. China is the first among the four largest emitters in the world to commit to carbon neutrality, and this goal has attracted widespread attention from countries around the world and various sectors of society domestically. It is bound to have a profound impact on China's medium- and long-term energy transition, technology investment, carbon reduction planning, and economic development.

According to statistics from the UNFCCC, over 100 countries or regions worldwide have mentioned carbon neutrality in relevant documents, with 29 (including 27 EU countries) providing official commitments through formal channels, covering more than 50% of global carbon emissions. Although countries differ in their understanding of carbon neutrality, specific goals, and progress in promoting it, they all reflect a shared determination to address climate change challenges globally.₂According to the IPCC's Special Report on Global Warming of 1.5°C, 'carbon neutrality' can be defined as a state of net-zero carbon dioxide (CO2) emissions achieved by offsetting anthropogenic CO2 emissions globally through natural or human-induced carbon removal (CDR).₂This article will advance research based on this definition and connotation.

Existing research and facts indicate that China's carbon neutrality aligns closely with the emission reduction requirements of the global 1.5°C temperature control target. At the global level, the 1.5°C target requires countries to jointly undertake substantial emission reduction activities, reducing global anthropogenic net emissions in 2030 to 55% of the level in 2010 and achieving net-zero emissions around 2050. At the national level, under differentiated emission budget allocation scenarios, the 1.5°C temperature control requires China to achieve near-zero emissions around 2060. Therefore, although there is currently a lack of comprehensive literature assessing China's path to achieving carbon neutrality systematically, there is considerable research surrounding the 1.5°C temperature control target that can provide useful references for evaluating carbon neutrality.

Given that the carbon neutrality target is similar to the requirements of the 1.5°C temperature control target at the national level, what is the difficulty in achieving China's carbon neutrality target? Is it as challenging as achieving the global level's 1.5°C temperature control target? What policy choices can lead to its realization? What are the energy technology evolution scenarios for achieving this target? What roles do various energy technologies play in reaching this goal? From a long-term perspective, does achieving the carbon neutrality target have economic feasibility? Are strategies for addressing climate challenges consistent with stable economic growth objectives? This article will comprehensively assess policy choices and technically feasible paths for achieving China's carbon neutrality target through a technology-driven energy-economy-environment integrated model.

Integrated Assessment Model for Technology Expansion

The assessment of the carbon neutrality target for 2060 involves complex issues across multiple system dimensions such as economy, energy, and environment over a long time scale. Integrated assessment models (IAMs) are powerful tools for conducting this research as they can provide dynamic feedback loops on economic, energy, and emission relationships under policy interventions. Based on this, this article will use a self-constructed CE3METL model to comprehensively assess China's carbon neutrality target. CE3METL is a dynamic intertemporal optimization model for a single region in China based on endogenous economic growth theory. The model consists of three main modules: macroeconomics, energy technology, and climate; its greatest feature is the introduction of a policy-driven Logistic multiple technology diffusion mechanism, which enables it to evaluate technological economic pathways from a top-down perspective.

The CE3METL model assumes that there exists a central planner with perfect foresight whose goal is to maximize social welfare based on given preferences. The accumulation of welfare comes from increases in per capita consumption across generations; thus, maximizing welfare is closely related to dynamic consumption flows and emission control efforts. The utility distribution between different generations depends on two factors: pure time preference and marginal utility of consumption (or consumption elasticity), which determines the choice of discount factor for intertemporal utility accumulation.

其中ν(t)=ν₀×e^-dνtas the discount factor. In CE3METL, production is based on input factors such as capital K, labor L, and energy E, modeled using a Cobb-Douglas function combined with constant elasticity of substitution forms; the production process is as follows:

where Y represents output; α and β measure factor productivity and autonomous energy efficiency improvement levels (AEEI), respectively. The population growth trajectory is exogenously given; capital stock is determined through optimization of investment and consumption flows. Similar to other integrated assessment models like DICE, it assumes that economic output is a single composite good; output flows include investment, consumption (government consumption and household consumption), payment for energy costs and carbon emission costs as well as imports and exports.

CE3METL assumes that imports IM and exports EX change according to GDP optimization paths; this is mainly achieved by setting upper limits for imports and lower limits for exports.

Energy costs EC include both energy usage costs (the sum of fossil energy costs ECF, low-carbon technology costs ECLC, and non-fossil energy technology costs ECNF) and also serve as a separate account to balance carbon tax Ftax_i和补贴Sub_jThe cost of policies, namely:

Here S_i(t) is the share of energy technology, F, LC, and NF represent fossil energy collection, low-carbon technology collection, and non-fossil energy technology collection respectively (specific technology list as shown in Figure 1). C_iis the usage cost of technology i, ζ_iis the proportion of fuel costs in low-carbon technologies, OC_jis the other power generation costs excluding fuel costs.

Existing research has shown that achieving strict climate goals relies on the contributions of low-carbon and negative carbon technologies [5,6]. Considering CCS can double the emission budget of fossil energy under the 2°C temperature control target within this century and the degree of developability of existing fossil energy reserves [20]. By 2050, negative emission technologies can provide 28EJ (10^18 joules) of electricity annually, sequestering 2.5 billion tCO₂, by 2100, this value will increase to 220EJ and 4 billion tCO₂[21]. Therefore, to scientifically assess the realization path of carbon neutrality goals, it is urgent to further enrich the energy technology details of the CE3METL model, namely incorporating representative low-carbon and negative emission technologies into the existing energy technology system. Based on this, this paper improves the existing energy technology system of the model by introducing three low-carbon technologies: supercritical coal power combined with CCS (SPC-CCS), integrated coal gasification combined cycle with CCS (IGCC-CCS), natural gas combined cycle with CCS (NGCC-CCS), and one negative emission technology: biomass combined with CCS (BECCS). A detailed technology list is shown in Figure 1. The substitution between technologies is mainly characterized by a modified Logistic multi-energy technology substitution evolution mechanism.

Given coal as the benchmark technology, any two technologies' substitution can be transformed into a substitution relationship between the benchmark technology and target technology i, with the ease of substitution determined by parameter a._i`S<sub>i</sub>is the market potential of technology, with 0≤`S_i<1. C_markis the cost of benchmark energy technology, here coal is selected as the benchmark technology. This mechanism is based on the Logistic technology diffusion model, adjusting the change in technology share over time to changes in market share concerning relative prices (i.e., the ratio of benchmark technology price to substitute technology price), while cleverly considering policy intervention effects such as carbon taxes and subsidies. At this point, the evolution path of technological substitution depends on the implementation strength of policies and changes in relative costs of technologies. From the expression of relative cost P_i, it can be seen that different policy interventions are applied to different technologies; for example, fossil energy technologies are taxed based on total costs while low-carbon technologies are divided into two parts: carbon tax on fossil fuel parts and subsidies based on other cost parts; non-fossil energy technologies can receive unified subsidy incentives. Given Ctax and Ccoal as the tax rate for coal and comprehensive usage cost respectively, then the carbon tax rate for other technologies can be obtained as follows:

where ξ represents the corresponding carbon emission factor for each technology; based on their respective usage costs, specific carbon tax levels can be calculated:

Endogenous technological advancement is a primary means to correct result deviations caused by purely exogenous technological progress [5]. This paper adopts a technological learning curve method based on 'learning by doing' effects to endogenize technological progress. The essence of this method is that as production scales expand, production experience or knowledge gradually accumulates, which in turn promotes technological improvements and reduces production or technological usage costs. Therefore, the endogenous technological progress process can be described as:

Here KD is knowledge capital stock, b_iis the learning index for technology i, determined by an exponential relationship with learning rate lri, namely lr_i=1-2.^-biIt is worth noting that knowledge capital needs to consider depreciation effects during intertemporal accumulation processes just like traditional capital; thus, current knowledge capital should be the net value after deducting obsolescence from last period's knowledge stock plus new knowledge flow.

On a global scale, characterizing the closed-loop feedback relationship between climate systems and economic and energy systems is one of the main tasks in comprehensive assessment modeling. This relationship includes carbon cycles, formation of radiative forcing flows, temperature response relationships, and climate feedback damages. For regional models, this paper simplifies the climate system module by only considering exogenous natural carbon emissions and endogenous anthropogenic carbon emissions; it can be obtained by summing up various carbon-containing energy's carbon content multiplied by their corresponding consumption amounts. Assuming non-fossil energy has zero emissions completely, then anthropogenic carbon emissions Emisanth can be calculated as follows:

where ξ_i, ξ_{, k}, and ξ_{, respectively represent carbon emission factors for fossil energy technologies, low-carbon energy technologies, and negative emission technologies.}分别表示化石能源技术、低碳能源技术和负排放技术的碳排放因子。

2 Data and Processing

 

The model calculations in this paper take 2010 as the initial year; from 2010 to 2020 is a calibration period; policies start implementation in 2020; and the model examination period is from 2020 to 2060 to report optimization results for the entire carbon neutrality target period. Macroeconomic data such as output, consumption, investment, imports and exports mainly come from China's Statistical Yearbook; various types of energy consumption data come from China's Energy Statistical Yearbook; carbon emission factors come from IPCC's revised carbon emission inventory [22]; exogenous population growth paths are set according to World Bank's latest expectations. More model data introductions and parameter settings can refer to Duan et al.'s latest published articles [5,19].

Due to significant differences compared to previous model versions, this paper's model reconstructs CE3METL's energy technology system by newly introducing low-emission and negative-emission technologies that may play a key role in achieving carbon neutrality goals; therefore, here we mainly introduce these technologies' cost data and technical parameters. For three low-carbon technologies, their costs consist of fuel costs and other costs; the former increases with rising fuel (coal and natural gas) prices. This paper assumes that fuel costs (including CCS energy consumption) account for an average of 60% of total supply costs; other costs include operating costs, transmission/distribution costs, and CCS operating costs (carbon capture, transportation, storage as well as carbon leakage detection and management costs). For negative emission technology BECCS, its cost includes biomass supply costs and CCS operating costs. Since supply costs for fossil energies and biomass non-fossil energies have already been well established in CE3METL model settings; thus new cost uncertainties mainly arise from CCS technology. Many studies have reported CCS cost information for representative demonstration power plants [23,24], this paper organizes relevant data as shown in Table 1.

 

The technical cost in CE3METL is the levelized total cost that includes energy supply and carbon treatment. The trend of fossil energy cost changes is set exogenously in a linear manner based on resource scarcity and historical price fluctuation information, while the carbon treatment cost is mainly averaged from the cost information in Table 1. Other important parameters include CCS capture efficiency and technology learning rate. For the former, it is set at 85% based on demonstration power plant data, from which the carbon emission factors for three low-carbon energy technologies can be derived. The combustion and furnace configuration of BECCS technology is assumed to use 100% biomass raw materials, although the process efficiency may only be 24.6%, but the negative carbon factor can reach as high as 1545gCO.₂/kWh. For the latter, traditional methods usually utilize the learning effects of flue-gas desulfurization technology to set the dual-factor learning rate for CCS technology, with learning rates for 'learning by doing' and 'learning by research' estimated at 7.1% and 6.6%, respectively. Kang et al. updated the valuation based on specific demonstration power plant data, indicating that the levelized cost learning rates for SPC-CCS, IGCC-CCS, and NGCC-CCS are 0.024~0.084, 0.088~0.182, and 0.045~0.100, respectively. This paper takes the median values of 0.054, 0.135, and 0.073.

Results and Analysis

 

The policy options designed in this paper mainly include carbon taxes on fossil energy, subsidies for low-carbon, zero-carbon, and negative-carbon energy technologies, as well as combinations of carbon tax and subsidy policies. Accordingly, several scenarios are set up: first is the reference scenario, which only considers current emission reduction policies (implied through calibration via energy consumption, carbon emissions, etc.) without considering new carbon pricing or subsidy policies; this scenario serves as a comparative reference for policy scenarios. The second is the carbon tax policy scenario, with policy strength referencing the requirements for achieving the goals of the Paris Agreement. Finally, there are subsidy policies and their combination with carbon tax policy scenarios; the model assumes that subsidy funds come from carbon tax revenue, with specific scenario setting details discussed in each subsection.

Reference Scenario Results

To ensure the credibility of the model's policy optimization results, it is necessary to calibrate key indicators such as economic growth and energy consumption based on historical data on one hand, and to set future indicator expectations by widely referencing existing research and expert opinions on the other hand. In terms of macroeconomics, based on statistical yearbook data, it can be estimated that the average annual growth rates from 2010-2015 and 2015-2019 are 8.38% and 6.74%, respectively; considering the impact of the pandemic on the economy in 2020, it is expected that the average annual growth rate from 2015-2020 will be lower than 6.74%. Accordingly, the CE3METL model calibration obtained average GDP growth rates of 8.32% and 6.71% for two time periods respectively, fully reflecting China's historical economic growth situation. Based on this, this paper further anticipates future economic growth paths. Considering potential rebound growth after the pandemic, this paper estimates that the average economic growth rate from 2020 to 2030 could reach 5.68%, gradually falling back to 3.25% from 2040 to 2050 and 2.72% from 2050 to 2060.

Currently, many domestic and international literatures have provided expectations for China's future economic growth trends, including reports from UNDP, IEA, Tsinghua University, etc., but their studies have not considered the short- to medium-term impacts of the pandemic on macroeconomics; looking at economic growth expectations after 2030, this study's results converge with these studies.

In terms of energy sector forecasts, multiple reports indicate that China's primary energy consumption will peak between 2035 and 2045.₂Specifically, a report by China Petroleum Economic Research Institute (ETRI) titled 'World and China Energy Outlook by 2050' indicates that China's primary energy consumption will peak in 2035, with fossil energy peaking in 2030.

The results from China's Academy of Engineering's Energy Production and Consumption Revolution Research Group suggest that China's primary energy consumption will peak around 2040; this aligns with a recent report released by State Grid Energy Research Institute which expects a peak value of about 5.7 billion tons of standard coal corresponding to a fossil energy peak value of about 4.3 billion tons.

In comparison, Tsinghua University's research on China's carbon development strategy gives a relatively later peak time for primary energy at around 2050 with a higher peak value level of about 6.2 billion tons of standard coal.₂The expected energy consumption structure provided in this paper shows that primary energy consumption will peak around 2045 at approximately 5.75 billion tons of standard coal while fossil energy will peak around 2035 at a peak value of about 4.6 billion tons; this result clearly aligns with expectations from various institutions.

The carbon emission path under reference scenario is shown in Figure X; results indicate that China's CO₂emissions will peak around 2035 at a level of approximately 11.9 billion tons. Here, carbon emissions are primarily caused by energy consumption but also include exogenous emissions resulting from land use changes.

So, what exactly is the reason for the existence of this gap? Is it due to insufficient efforts to reduce coal in the economic system or the underdevelopment of non-fossil energy? Therefore, further analysis needs to focus on the evolution of the energy structure under different policy scenarios. The specific results are shown in Figure 7. It is not difficult to see that the implementation of carbon pricing policies has significantly accelerated the process of reducing coal in the energy system, specifically starting from 2020, the year the policy was implemented, carbon emissions began to decline significantly. In the reference scenario, coal demand in 2060 is projected to be 1.92 billion tons; under the carbon tax scenario T25%, this demand is compressed to 1.08 billion tons, a decrease of 43.8%. In a stricter T55% scenario, coal demand further drops to 330 million tons, accounting for only 6.2% of total primary energy consumption, indicating that the economic system is close to complete decarbonization. Thus, it can be seen that under carbon pricing incentives, substantial contributions from reducing coal in the energy system provide significant emission reduction potential. Figure 7 also shows that under carbon pricing policies, renewable energy technologies have been vigorously developed, especially for wind and solar power. In a low carbon price scenario, by 2060, nuclear power, wind power, and solar power will account for 7.9%, 15.2%, and 19.8% of primary energy consumption respectively, with non-fossil energy consumption exceeding 64%. Under the strictest carbon pricing policy, these shares will increase to 8.3%, 24%, and 17.5% respectively, corresponding to a total non-fossil energy consumption share of 71%, which is more than 30% higher than in the reference scenario. Therefore, it is also evident that the development of non-fossil energy contributes significantly to carbon reduction under carbon pricing incentives.

Further observation of Figure 7 reveals that under different carbon price scenarios, oil and gas consumption remains stable, while natural gas has a higher degree of decarbonization and a smaller share of consumption, indicating it is not a major contributor to residual emissions. Therefore, carbon emissions caused by oil consumption should be the main reason for the emission reduction gap in achieving carbon neutrality targets. This means that a policy model based on uniform taxation according to carbon content cannot achieve oil reduction in the energy system. If future oil prices do not show a significant increase above coal prices, long-term stable oil consumption will become a major challenge for China in achieving its carbon neutrality target by 2060. Of course, this result is not only supported by this model but also has ample evidence in research by Duan et al. Specifically, their study analyzed results from eight representative integrated assessment models from both domestic and international sources, with relevant data coming from IIASA's ADVANCE database and China's model comparison project database. The results show that only the AIM and IMAGE models provided a gradually declining oil consumption volume; under most models, China's future oil consumption structure tends to stabilize, with REMIND, GCAM-TU, and IPAC models showing significant growth in oil consumption share. These cross-model results greatly enhance the findings of this study.

3.3 Can non-fossil energy subsidies ensure the achievement of carbon neutrality targets?

The report from the State Grid Energy Research Institute suggests that by 2060, China's share of non-fossil energy could reach 81%, with wind and solar contributing over 50%. Does this mean that continuing to strengthen non-fossil energy development can help achieve carbon neutrality? To this end, this section will introduce subsidies for non-fossil energy incentives based on the aforementioned carbon price policy scenarios; here non-fossil energy includes renewable energy and low-carbon or negative-carbon energy technologies, with subsidy funds mainly coming from carbon tax revenues. The subsidy rates are set at 10%, 20%, and 30%, based on research by Mo Jianlei et al., corresponding to three scenarios: S10, S20, and S30. In combination with moderate carbon price scenarios and subsidies, China's share of non-fossil energy consumption could reach 80% by 2060; therefore, we mainly select results from this combined scenario for discussion: T40S10, T40S20, and T40S30.

It is not difficult to see from Figure 8 that introducing subsidy policies can bring about certain emission reduction effects (reducing emissions by 1.35 billion tons CO₂),₂but under the combination scenario of carbon price and subsidies, carbon emissions in 2060 still do not reach near-zero or neutrality; the gap is 3.1 billion tons CO₂.₂This result also confirms existing research conclusions that even with full utilization of alternative energies, China's demand for negative emissions to achieve carbon neutrality will still be as high as 2.5 billion tons/year. This gap can be explained from three aspects: first, targeted subsidies have a significant incentive effect on the development of non-fossil energy technologies; however, over time this effect diminishes marginally; second, under equal subsidies, low-carbon and negative-carbon technologies have not achieved leapfrog development; during the later stages of subsidy implementation, low-carbon technology market shares were even squeezed out by other technologies to varying degrees; thirdly, oil consumption's rigidity has not decreased due to incentives from non-fossil energy subsidies; similar results were observed under pure carbon pricing policies where stable oil consumption remains a major reason for difficulty in achieving carbon neutrality targets (Figure 9). On one hand, China's oil consumption is concentrated in transportation sectors where CCS technology is difficult to apply; on the other hand, an equal subsidy mechanism cannot currently incentivize negative-carbon technology development.

3.4 Policy choices and technological paths for achieving carbon neutrality

From the above analysis it can be seen that achieving carbon neutrality targets mainly requires policy efforts in two areas: first, promoting technological substitution to break down oil consumption rigidity; second, providing strong policy incentives for low-carbon/negative-carbon technologies such as biomass energy. In fact, due to bottlenecks in energy storage technology development, renewable energies such as wind power and photovoltaics will also struggle to serve as stable base-load energies for quite some time; at this point, combined technologies of biomass energy and CCS provide a realistically reliable technological choice for simultaneously achieving stable energy supply and emission reductions. To this end, this section introduces constraints on reducing oil consumption while strengthening non-fossil energies' substitutability for oil and gas. At the same time adjusting subsidy strategies: on one hand canceling subsidies for all renewable energies according to current policies regarding declining new energy subsidies; on the other hand investing all subsidy budgets into negative emission technologies such as BECCS while setting up subsidy scenarios at rates of 40%, 45%, and 50% (BE40, BE45 and BE50). Numerical experiments show that under this level of subsidy support only lower levels of carbon pricing policies (15%, 25%, and 35%, corresponding to T15, T25 and T35 scenarios) are needed to fill net-zero emission gaps; at this point initial carbon price levels drop as low as ¥128/tCO₂.₂~¥298/tCO₂₂, by 2060 each ton CO₂ will only cost ¥231~¥540.₂Through comparative analysis of nine combined policy scenarios three policy choices under minimum cost framework were obtained: T15BE50、T25BE45、and T35BE40.

As shown in Figure 10 despite differences in emission trajectories under different policies all can achieve carbon neutrality vision before 2060 while realizing negative emissions between 100 million tons~400 million tons in target year.₂The peak emission time is between 2030 and 2035, with peak emissions ranging from 10.7 billion tons to 11.7 billion tons. It can be seen that the achievement of the carbon emission peak target in 2030 may not have an absolute correlation with the achievement of the carbon neutrality target. The substantial development of negative emission technologies can significantly reduce the short- to medium-term carbon reduction pressure, while also alleviating the dependence on the uncertain large-scale development of renewable energy technologies to a large extent. This conclusion is also consistent with research on a global scale. The energy technology path under the achievement of carbon neutrality targets is shown in Figure 11. First, carbon neutrality requires fossil energy to achieve substantial decoupling from the economy starting in 2040, and by 2045, non-fossil energy will gradually dominate the energy market. Second, the carbon neutrality target is basically consistent with the non-fossil energy development target for 2030, at which point non-fossil energy will account for 24.7% to 26.3% of total primary energy consumption; by 2050, the share of non-fossil energy could reach as high as 75%, and by 2060, this figure will approach 100%, fully achieving decoupling of economic growth from carbon energy consumption. Furthermore, at different points in time, the consumption structure of non-fossil energy varies. In 2030, hydropower will serve as the mainstay of non-fossil energy, accounting for over 11.5%, followed by wind and solar power, which will account for 6.1% and 4.4%, respectively; this structure will largely continue until 2040, when their shares will be 14.8%, 10.9%, and 10.5% (corresponding to the T35BE40 scenario). By 2050, solar power consumption will begin to exceed that of hydropower and wind power, with their total share exceeding 55%, while BECCS will gradually develop, potentially accounting for up to 4.9% of energy supply; by 2060, solar and wind power will still be the main sources of energy supply, together accounting for 53%, followed by hydropower and BECCS, with corresponding consumption shares of 13.7% and 12.5%. In fact, existing research assessment results show that the global BECCS carbon removal potential reaches 7.3 trillion tons.₂(Cumulative value from 2016 to 2100), while as early as 2013, the potential share of biomass in China's primary energy consumption could reach 40%.

3.5 Economic Analysis of Achieving Carbon Neutrality Targets

The greatest advantage of comprehensive assessment models in studying climate policy is that they can establish a closed-loop feedback system from policy intervention to emission control target assessment, then to technology path evolution and policy cost analysis. In particular, policy costs are a key factor affecting policy feasibility and are therefore a focus for decision-makers. The results in Figure 12 show that even if policy costs (measured as a percentage of GDP loss) for achieving carbon neutrality targets present an inverted 'U' shape over time—indicating that the negative impact during the initial implementation phase is significant, potentially reaching up to 4% of GDP—over time, as new energy technologies gain development space, their costs and competitive advantages will enhance their substitution for traditional energy technologies, leading to an increase in the economic system's adaptation and digestion capacity towards policy shocks and a gradual decrease in policy costs. The results indicate that zero policy costs can be achieved around 2050; thereafter, the impact of carbon neutrality policies reflects positive incentives for economic development. Moreover, the greater the current policy intensity, the stronger the positive incentive effect; by 2060, the maximum positive impact could reach up to 2.4% of GDP. From a total policy cost perspective, by 2060, the cumulative economic cost caused by carbon neutrality policies will account for between 0.3% and 1.9% of cumulative GDP (at a discount rate of 5%). This result is slightly lower than Tsinghua University's assessment of China's investment costs to achieve a temperature control target of 1.5°C; the latter estimates that achieving this target will require China's annual new investment to account for 2.5% of GDP.

4 Conclusion

 

China's commitment to achieving carbon neutrality by 2060 elevates climate challenges to a mid- to long-term national strategic height, which will inevitably have far-reaching impacts on our future economic development, energy transition, and emission reduction policy formulation. Against this backdrop, conducting a systematic and comprehensive assessment of this goal will have significant practical significance. Based on this, this paper improves the comprehensive assessment model CE3METL's energy technology system by considering low-carbon and negative-carbon technologies that have a critical impact on achieving strict carbon reduction targets and comprehensively assesses possible policy choices, energy technology optimization paths, and policy costs for achieving China's carbon neutrality target by 2060, providing policy recommendations that help achieve this strategic goal.

The study first calibrates and anticipates China's future economic growth status based on historical economic growth and energy consumption trends while considering the short-term impact of the pandemic on the economy. It points out that from 2020 to 2030, China's average annual GDP growth rate can stabilize at over 5.6%, gradually decreasing to about 3.25% from 2040 to 2050 and further down to about 2.72% from 2050 to 2060; by mid-century, China's per capita GDP is expected to grow approximately sixfold compared to that in 2010. At the same time, carbon emissions are expected to peak around or before 2035 at a corresponding peak level of about 11.9 billion tons, which is generally consistent with many representative IAM model results both domestically and internationally.

This paper simulates various policy choices' impacts on achieving carbon neutrality targets. The study finds that first, a simple carbon pricing policy is insufficient for China to achieve its carbon neutrality target by 2060 even if carbon pricing levels reach those necessary globally to fulfill commitments under the Paris Agreement. Second, combining carbon pricing with universal subsidies for non-fossil energy can somewhat incentivize low-carbon transitions in energy but still falls short of achieving China's carbon neutrality target; by 2060, there will still be a gap reaching up to about three billion tons CO2.₂Furthermore, the study identifies relative rigidity in oil consumption as likely one of China's main challenges in achieving its carbon neutrality target. Finally, combining carbon pricing policies with targeted subsidies for negative emission technologies can help China successfully achieve its carbon neutrality goal by 2060; while there are no strict requirements regarding peak emissions paths for achieving neutrality targets, it is necessary to shorten high-emission 'plateau periods,' with peak levels expected between approximately ten point seven billion tons and eleven point seven billion tons.

The assessment results provide an energy technology path under scenarios where carbon neutrality targets are achieved. Fossil fuels are expected to achieve net zero around or before 2055 (allowing time for most coal-fired power plants to gradually retire within their lifecycle). The study finds consistency between achieving non-fossil energy development goals for 2030 and carbon neutrality targets; a requirement for a share of non-fossil energy at around twenty-five percent can serve as an interim goal towards carbon neutrality. Starting from around or after2045 , non-fossil energies will dominate the energy market with their consumption share potentially reaching seventy-five percent; by2060 , it is expected that nearly one hundred percent clean energy transformation can be achieved , at which point solar and wind power will become major contributors , together accounting for fifty-three percent , while hydropower's consumption share remains at thirteen point seven percent , with negative emission technology BECCS also contributing twelve point five percent . From a policy cost perspective , overall economic costs associated with achieving carbon neutrality targets are lower than those required for meeting Paris Agreement temperature control goals , with maximum immediate economic losses reaching up to four percent of GDP , while cumulative GDP losses by2060 do not exceed one point nine percent ; policy costs significantly decrease over time , allowing China to gradually enjoy developmental dividends brought about by carbon neutrality policies after mid-century , entering an era of rapid economic growth post-neutrality.

Based on this, this article proposes policy recommendations to address the challenges of medium- and long-term carbon neutrality. First, it should be acknowledged that the design and implementation of short- to medium-term phased goals are crucial for achieving long-term climate strategic objectives. For example, the 25% non-fossil energy development target for 2030 is highly consistent with the carbon neutrality target for 2060. However, the peak emission target and the carbon neutrality target do not have a strong causal relationship. This actually requires relevant departments to fully understand the relationship between relative and absolute targets, as well as between short- to medium-term and long-term goals. This relationship largely depends on future technological development and policy choices to achieve these goals. The research in this article shows that China's carbon emission path may not necessarily present a 'hump' shape (where an early peak means a greater possibility of achieving carbon neutrality); it could also be a 'flat parabola' shape (where the timing of reaching peak emissions does not determine the difficulty of achieving carbon neutrality). This means that when making decisions about short- to medium-term goal achievement, the government should rely on the realities of energy technology development to minimize inconsistencies between transitional and long-term goals. Furthermore, an overly early peak in carbon emissions will inevitably raise peak levels, which may lead to new carbon lock-in effects, thereby hindering the achievement of long-term carbon neutrality.

Secondly, the research results did not find a natural high substitutability between non-fossil energy and oil consumption; carbon emissions caused by oil consumption are likely to become an obstacle to achieving China's medium- and long-term climate policy goals. This provides two insights: first, considering the high carbon content of coal and its absolute share in the energy structure, the current widespread view that decarbonization equates to coal phase-out is not without merit. However, in the long term, this article suggests shifting some attention to controlling oil consumption, which is often overlooked. Second, there is a need for strong policy-level promotion of the substitution of oil with non-fossil energy, introducing reduction mechanisms to achieve oil phase-out; this corresponds to a series of specific industry development policies related to oil consumption, industrial structure adjustments, and technological transformations, such as planning for oil phase-out in transportation systems and policy incentives for hydrogen fuel cell technology development. Additionally, configuring biomass energy with CCS technology is also significant for reducing oil consumption; thus, policies need to pay more attention to biomass management and CCS technology development (e.g., shifting tendencies in subsidy incentives).

Again, policy costs do not necessarily become the main concern regarding whether carbon neutrality targets can be achieved. On one hand, policy choices and combination strategies are crucial for reducing policy costs; high negative economic impacts mainly stem from strong carbon price shocks, while introducing targeted energy subsidies can reduce reliance on high carbon prices while ensuring equivalent goal achievement, thereby lowering policy costs. On the other hand, the negative impact of carbon neutrality policies on the economy mainly manifests in the short- to medium-term (i.e., during the painful transition period). In the long run, the economic system will ultimately reap dividends from technological transformation and structural adjustments, achieving green high-quality growth in the post-carbon neutrality era.

Although this article conducted a closed-loop comprehensive assessment of China's carbon neutrality targets based on a system integration model early on and obtained meaningful new findings and policy insights, certain limitations still exist. First, the improved CE3METL model sets cost evolution paths for three main fossil energy technologies in a linear growth form based on the scarcity of depletable resources and historical price fluctuation trends but does not consider cost uncertainties caused by fluctuations in energy markets. This significantly affects the competitive substitution of non-fossil energy for traditional energy under policy interventions. Second, considering that oil consumption is mainly concentrated in the transportation sector, managing mobile emission sources presents considerable difficulties; therefore, the model does not consider carbon capture technologies related to oil combustion nor does it introduce technological options such as hydrogen storage and fuel cells that could play a key role in substituting transportation oil consumption. This makes it difficult for oil reduction at a mechanism level to correspond with specific technologies. Thirdly, this study explores the important role of negative emission technologies in achieving carbon neutrality targets; however, large-scale development of biomass energy requires weighing a series of related issues such as biomass development potential, land use, food security, and water security—all of which need further research for strengthening and improvement. Finally, in the carbon neutrality energy transition path provided in this article, wind energy and photovoltaic solar technology account for a significant proportion; however, this does not consider constraints from mineral resources. In fact, renewable energy relies much more heavily on key mineral resources than traditional fossil energy does. Therefore, introducing constraints from mineral resources may likely lead to a decrease in the contribution ratio of clean energy dominated by wind and solar power; this also relies on developing comprehensive assessment models under resource constraint mechanisms and research related to policy optimization.