Global implications of uncertainty in China’s climate policy delivery

Domain: chemistry
Published: 2026-03-06
Editorial outcome: minor_revision

Abstract
The delivery of China’s climate policy has substantial global implications, yet persistent gaps between policy
targets and implementation raise concerns about policy credibility. This study presents a structured credibility
assessment of 292 targets across 58 national climate and energy policies, including China’s updated 2035 NDC.
We apply a morphological scenario framework to examine interactions between policy uncertainty and socio-
economic and technological drivers, embedding these scenarios in an integrated assessment model under two
global contexts: one aligned with current NDCs and one consistent with global net-zero ambition. We find that
timely or accelerated delivery of China’s net-zero target could partially offset insufficient ambition elsewhere by
mid-century. Full delivery by 2050 reduces global CO₂ emissions to 13 Gt, compared with 23 Gt without such
policies. Policy delivery uncertainty alone could imply up to ~500 GtCO₂ difference in cumulative emissions by
2100 (~0.17 °C warming).

Introduction
Recent years have seen substantial changes in China’s climate governance, reflecting a growing effort to align
decarbonization goals with economic development. Following its updated Nationally Determined Contribution
(NDC) targets in 2021, China incorporated carbon reduction into national planning for the first time through the
14th Five-Year Plan (2021–2025) 1,2. The introduction of the “1+N” policy framework, combining a national
carbon neutrality roadmap (“1”) with sectoral and regional action plans (“N”),has since guided coordinated
implementation across multiple levels of governance3,4. In September 2025, China further updated its NDC for
2035, introducing for the first time an absolute greenhouse gas (GHG) emissions reduction target of 7–10%
below the peak level by 2035.

As the 14th Five-Year Plan is in its final year in 2025, persistent gaps between central government directives and
on-the-ground implementation (SI Table 3) raise critical questions about the effectiveness of China’s climate
policy architecture and the credibility of its multi-level climate targets. As the world’s largest emitter, accounting
for 32% of global CO₂ emissions in 2024,and the largest consumer of coal and leading importer of oil and gas,
the implications of China’s policy delivery extend well beyond its national borders5. In the absence of
international ambition - especially as major economies like US stall or roll back their commitments,
understanding the role and limits of unilateral action becomes essential.

Our analysis of China’s climate policy delivery proceeds in three steps. First, we develop a systematic assessment
of the credibility of China’s climate and energy policy targets. While policy credibility has been increasingly
discussed in the academic literature, it remains weakly defined and rarely quantified in a systematic way6–9. We
adapt the framework proposed by Rogelj et al. to suit the characteristics of China’s political system 6. A total of
292 numerical targets across 58 policy documents are reviewed. Of these, 47 quantifiable targets are filtered
and implemented in the energy system model (see Methods). Target’s credibility is evaluated using three
indicators: (i) the governance level at which it is set, (ii) whether it is explicitly stated in national Five-Year Plans,
and (iii) the degree to which it is on track to be achieved. These indicators are combined into a weighted
credibility score that reflects varying levels of policy credibility, ranging from targets that have already been
achieved to those assessed as low credibility.

These differences in delivery reflect deeper structural trade-offs in China’s climate policymaking, particularly the
tension between long-term net-zero commitments and near-term socio-economic development priorities.
China’s case is distinctive due to the government’s central role in shaping social and economic development,
including setting specific growth targets and implementing population planning policies. This strong state-led
approach means there is an interplay between broader system-level changes (such as GDP and population) and
domestic policy delivery, which Shared Socioeconomic Pathways (SSPs) tend to overlook10. As a result, most
global integrated assessment model (IAM) projections of energy service demand that rely on SSP-based drivers
lack adequate country-specific diversity.

To address this gap, the second stage of our analysis employs a morphological scenario framework (see
Methods) to construct internally consistent narratives of China’s energy future, the implications of which are
assessed using the TIAM-UCL. We develop four scenarios that reflect the interplay between policy
implementation and broader system-wide factors, including economic development, demographic trends,
technological innovation. The scenarios capture a diverse set of futures for China’s climate governance,
alongside corresponding shifts in socioeconomic and technological development (see Methods and SI section 2).
Great Wall (GW) reflects a constrained future marked by economic stagnation, declining population, and stalled
climate progress. Red Sun (RS) envisions strong economic growth, driven by real estate market recovery and
urbanisation, while climate goals are deprioritised. Calm Sea (CS) presents a balanced pathway where China
maintains moderate economic growth while fully implementing current climate policies. Green Lights (GL)
depicts a future where China demonstrates climate leadership while maintaining high economic growth. This
range of scenarios reflect increasingly effective delivery of sectoral and national policy targets, informed by the
credibility assessment.

Our modelling indicates that China’s current policy framework has set its energy system on a path of
accelerating electrification, driven by credible efforts in EV adoption and renewables expansion. However, the
lack of explicit, enforceable fossil fuel phaseout targets, especially for coal, continues to undermine climate
goals. Ambiguities in industrial policy and weak enforcement indicate a persistent misalignment between
national targets and local growth incentives, weakening sectoral credibility. Achieving net-zero before 2060 could
require electrification rates above 48%, variable renewables supplying nearly 68% of electricity, and substantial
electrification and coal reduction in the industrial sector. Our results provide important insights to inform the
design of China’s next Five-Year Plan and highlight the need for central government to clearly assign
responsibilities across regions and strengthen incentives for credible local implementation.

Finally, in our third step, we quantify the global impact of China’s policy uncertainty by embedding our four
national scenarios into two global contexts, either limited to achieving current NDCs or delivering the net-zero
target (see Methods). This framework highlights China’s potential role in global decarbonisation, providing a
clear assessment of its relative contribution to such a target. Our modelling suggests that differences in China’s
climate policy delivery alone leads to cumulative global emission variation of up to around 500 GtCO₂ by 2100,
which translates to increased or decreased warming of up to 0.17 °C. While more ambitious domestic policy
could see China compensating for the failure of other nations to fully deliver on their mitigation objectives by
mid-century, they cannot fully offset prolonged global inaction.

This study contributes to the literature in three key aspects. First, compared to Chinese government official
assessments of policy progress, our structured credibility assessment offers an evaluation of climate and energy
targets, uniquely linked to integrated assessment modelling. In contrast to most China-focused scenario studies,
which adopt normative assumptions and lack consistent socioeconomic narratives, our approach systematically
decodes national policy rhetoric into four exploratory scenarios reflecting China’s development priorities and
constraints11–13. Our study evaluates the potential implications of China’s new 2035 NDC for its energy transition
and global decarbonisation using an energy modelling framework. Second, it bridges a critical gap between
global IAM research and detailed China-specific modelling of policy scenarios, providing a structured approach
to assess the role and limits of unilateral action under varying global contexts14,15. Third, it offers empirical
insights into China’s policy implementation challenges and offers actionable evidence to inform future national
governance and international cooperation.

Results
Chinese Decarbonization and Energy Transformation
Existing Policy Efforts Ensures High Certainty in Electrification and Renewable Expansion
Our scenario analysis indicates that existing policy efforts over the past five years have driven system-wide
electrification and renewable power expansion (Figure 1. (a) and (b)). As policies progressively strengthen from
the Great Wall (GW) to the Green Lights (GL) scenario, and as more high-level climate objectives are assumed to
be successfully achieved, the electrification rate (share of electricity of final energy consumption) rises to 48%
under GL by 2050, nearly doubling the 2020 level (Figure 1. (a)). While current power sector policies lack explicit
coal control targets, China’s overachievement with respect to wind, solar capacity installation targets, combined
with declining costs, has accelerated and could continue to accelerate renewable deployment. Even in the most
conservative scenario (GW), coal could phase out by 2060, while wind and solar generation reaches 7.6 PWh by
2060—eight times the 2020 level.

However, the pace and depth of power sector decarbonisation is still strongly shaped by uncertainty in national
net-zero target achievement. Compared with the Calm Sea (CS) and GL scenarios, achieving net-zero target
requires an immediate reduction in coal generation from current levels and a more ambitious scale-up of wind
and solar generation, reaching 14.8 PWh in CS and 17.5 PWh in GL, both accounting for around 68% of total
electricity generation by 2060 (Figure 1. (b)). Our sensitivity analysis suggests that to achieve the 2035 GHG
reduction targets, coal generation must be phased out more rapidly, resulting in an additional ~1 PWh annual
reduction in 2040 compared with the main cases of RS and CS (see SI Figure 5. (a))

Policy Credibility Differences Create Uncertainty in Industrial Decarbonisation
Although the expansion of electrification improves system-wide energy efficiency, existing efforts in renewable
power and electric vehicle (EV) alone is insufficient to deliver deep decarbonisation due to the weakness of
industrial policies. This weakness is reflected in two aspects: the limited credibility of current industrial
measures (e.g. steel scrap use, energy intensity reduction) and the absence of explicit coal reduction targets in
current policy landscape.

Without binding net-zero commitments in GW and RS, industrial coal consumption is estimated to grow to 37–
40 EJ per year by 2060 to support varying degrees of economic development (Figure 1. (c)). Under net-zero
ambition in CS and GL, industrial coal is constrained via accelerated electrification but still requires steep post-
2050 reductions, placing increased pressure on coal phaseout in the petrochemical sector and driving increased
demand for alternative fuels such as bioenergy and hydrogen (Figure 1. (a)). Taken together, the attainment of
China’s national net-zero goal is fundamentally dependent on a substantial industrial electrification and coal
reduction. Therefore, clearer coal phaseout targets and stronger enforcement of industrial decarbonisation
should be central to China’s next phase of climate governance.

Policy Ambiguity Heightens the Risk of Oil and Gas Lock-In in the Energy System
Compared to coal, oil and gas play limited but persistent roles in China’s energy system over the long term,
mainly as chemical feedstocks. Overall, oil and gas never exceed 20.8% (RS) or fall below 14.5% (GL) of the total
energy mix, and neither is fully phased out, even under net-zero scenarios, highlighting the challenge of
decarbonising hard-to-abate sectors.

Oil demand in China is primarily driven by the transport and industrial sectors. Given the country’s notable
progress in EV adoption, high-credibility EV policies are assumed to be implemented at an increasing rate from
GW to GL, leading to lower oil use in road transport. However, total transport-related demand remains uncertain
due to non-road sectors such as shipping and aviation. In the net-zero scenarios (CS and GL), oil is nearly fully
phased out from transport by the target year, positioning the sector as the greatest source of oil reduction but
also intensifying pressure to scale up bio jet kerosene in aviation and hydrogen and ammonia fuels in shipping
(Figure 1. (d)). However, lingering industrial demand, particularly for petrochemical feedstocks, remains the final
barrier to a complete oil phaseout.

Natural gas demand indicates distinct sectoral and temporal patterns across scenarios, shaped by various
drivers. While the government has not set explicit sectoral targets, it prioritizes residential and public service
use, allows the use for peak power generation, and restricts natural gas industrial use of methanol and urea
production16. This could result in increased seasonal and hourly fluctuations in gas demand. Given TIAM’s
limited temporal resolution, our results indicate that annual gas demand in the buildings sector remains
relatively stable, ranging from 2.4 to 3.5 EJ (15%–20% of total use). In GW and RS, additional demand comes
from gas-fuelled trucks and LNG bunkering, reflecting current market patterns. In contrast, net-zero scenarios
(CS and GL) require a complete transport phaseout and gradual reduction in industrial levels from 2040 onward.
Total gas use peaks at ~23.6 EJ (620 bcm) around 2040 if net-zero targets are achieved, as observed in CS and GL
(Figure 1. (e)).

Notably, China’s heavy reliance on imports (73% for oil and 40.9% for gas) adds complexity to energy policy
making and shapes government perspectives on the strategic role of these fuels17. Our policy assessment finds
that government attention is primarily focused on objectives related to energy security and system stability, such
as domestic production targets, pipeline infrastructure, and storage facilities. These priorities are reflected in
national planning and most policy targets are achieved by 2025 or are rated as highly credible (see SI Table 3).
This focus reflects an ambiguous stance on the long-term phaseout of oil and gas, increasing the risk of their
continued lock-in to the energy system. Given that China is the world’s largest oil and gas importer, its future
demand trajectory carries important implications for the pace of the global fossil fuel phaseout.

Global Implications of Uncertainty in China’s Climate Policy Credibility
Panel a-b in Figure 2 indicates that China plays a varying but consistent role across different global climate
ambition contexts. For a global net-zero context, annual CO₂ emissions decline from approximately 40 Gt yr⁻¹ in
2020 to between ~23 Gt yr⁻¹ and ~13 Gt yr⁻¹ by 2050, depending on uncertainties in China’s policy delivery.
Whether China prioritises economic growth without strong decarbonisation (RS) or pursues green development
(GL) shape its contribution to global climate outcomes. In the RS scenario, China’s share of global annual CO2
emissions reaches 32%–42% by 2050, posing a major challenge to global decarbonisation. In contrast, the CS
scenario sees China’s share fall from 32% in 2024 to 21%–30% by 2050 with timely net-zero delivery. If China
were to achieve net zero even earlier by 2050 as in GL, its share drops to zero, firmly positioning it as a global
climate leader.

Within each global context, uncertainties in China’s domestic policy pathways lead to a substantial spread in
global cumulative net CO₂ emissions and associated warming outcomes (Figure 2. (c) and (d)). By 2050,
cumulative emissions differ by ~75 GtCO₂ in both the NDC (1,031–1,106 Gt) and Net Zero (1,006–1,081 Gt)
groups. By 2100, these gaps widen further. Cumulative emissions range from 1,909 to 2,421 GtCO₂ under the
NDC context and from 1,153 to 1,634GtCO₂ under Net Zero, representing a potential 500 GtCO₂ difference
across Chinese scenarios. This magnitude is almost half of the IPCC-estimated 1,150 GtCO₂ global carbon budget
for a >50% chance of limiting warming to 2 °C from 2020 onwards, underscoring the scale of China’s global
climate leverage. Achieving the additional 2035 GHG emissions reduction targets set out in China’s updated NDC
could lower global cumulative CO₂ emissions by approximately 4–6 Gt (7% reduction case) and 6–10 Gt (10%
reduction case) across our scenarios (See SI Table 14).

Correspondingly, estimated warming from TIAM-UCL diverges more substantially, ranging from 2.46–2.63 °C
under the NDC context and 2.05–2.23 °C under Net Zero. These results demonstrate that internal uncertainties
within China, particularly regarding the credibility and delivery of its climate policies, as well as associated
societal and technological transitions, could shift global warming outcomes by as much as 0.17°C by the end of
this century.

However, whether China’s climate influence of ~0.17 °C alone is decisive in determining whether global warming
stays below 2 °C still depends on the level of climate ambition in the rest of the world. Our results indicate that
by mid-century, China's domestic actions can play a pivotal role in narrowing the ambition gap: emissions
trajectories under the NDC and Net Zero contexts partially overlap depending on China’s policy delivery (Figure
2. (c)). This suggests that China has the capacity to buffer or delay the global consequences of insufficient action
elsewhere—at least temporarily.

Nevertheless, China alone cannot fully offset the cumulative emissions and temperature differences that result
from divergent global efforts by 2100. Even under China’s most ambitious scenario (GL), cumulative emissions
and warming outcomes under the NDC context remain higher than under the net-zero context, underscoring the
structural limits of unilateral action, even from the world's largest emitter today, in the face of global inaction.
Therefore, in a world where the remaining carbon budget is rapidly shrinking, the credibility and timely delivery
of China’s internal climate policies are essential but must be complemented by stronger international ambition if
key temperature thresholds are to be preserved.

Methods
Credibility assessment of Chinese climate policy delivery
Existing Chinese policy-related scenario studies have primarily focused on overarching goals such as the NDC,
carbon peaking, and carbon neutrality targets11,12. These studies often assume the full and timely delivery of
policy measures, while overlooking the uncertainties associated with delayed, partial, or failed implementation,
particularly at the sectoral level. This paper takes a step toward closing that research gap by systematically
assessing the credibility of China’s sectoral climate and energy targets, providing a more realistic basis for
evaluating whether the country is on track to meet its decarbonisation goals.

The definition of ‘policy credibility’ varies in the literature 22,23. Furthermore, there is no standard assessment
framework for policy credibility across studies7,24. Fransen et al. rely on cross-country composite indices such as
the Climate Policy Score and the Climate Change Performance Index to capture policy adoption and outcome
gaps25. Peterson and van Asselt propose five indicators to assess country-level NDC implementation risks: NDC
ambition, institutional capacity, interest group opposition, policy inconsistency, and monitoring and
enforcement, highlighting common challenges in closing the implementation gap26. While these frameworks are
valuable for cross-country comparison, they are less suited to China’s context, where sectoral targets embedded
in central plans are the primary drivers of emissions outcomes. In this study, we build on Rogelj, J. et al, who
define credibility as “the level of confidence in a target delivery”, and extend their net-zero target credibility
assessment to national-level targets in China6.

The policies reviewed in this study encompass all climate- and energy-related documents issued by the Chinese
central government between 2020 and 2025, including action plans, implementation schemes, strategic
outlines, guiding opinions, technical guidelines, and standards. In total, 58 policy documents were analysed,
covering 292 numerical targets, including China’s updated 2035 NDC.

Given the quantification limits of the TIAM-UCL model and the fact that some targets are unmeasurable (e.g. all
ground vehicles and equipment at civil airports could strive to be powered by electricity by 2030), 47 targets
were filtered and included in our credibility assessment (See Figure 3. (b)). The detailed reasons and implications
for target non-quantifiability are discussed in SI Section 1. Target-level details are provided in the supplementary
data. In the context of China, we developed and applied a credibility rating based on three key policy
characteristics:

• Governance level at which policy is set: we replace the “legally binding” criterion in Rogelj et al. with
the governance level at which a policy is set, reflecting China’s hierarchical governance system, which is
driven more by executive authority than parliamentary law 27. Climate policies in China are typically
issued as soft regulations rather than hard laws, making the level of authority rather than legal
enforceability a more relevant indicator of credibility29 (see Figure 3. (a)). For example, the “dual-
carbon” targets (peaking carbon emissions by 2030 and achieving carbon neutrality by 2060), endorsed
by the Chinese Communist Party (CCP) and State Council, carry the highest authority and are made
binding at the national level. In contrast, policies from the National Development and Reform
Commission (NDRC) or ministries are often non-binding guidelines. Higher-level policies tend to receive
stronger institutional support, funding, and monitoring, making them more likely to be implemented
effectively30. Lower-level policies, by contrast, rely heavily on local interpretation and capacity, often
leading to inconsistent outcomes31. Thus, we treat targets with high-level endorsement and repeated
top-down reaffirmation as more credible.

• Clearly stated in a five-year plan: The 14th Five-Year Plan (FYP) plays a key role in China’s climate
governance by integrating environmental goals into national development priorities and setting clear,
strategic targets32,33. Moreover, the targets set in the 14th FYP are typically cautious and realistic, closely
tied to government performance evaluations. This connection enhances their executive nature, making
them more actionable and subject to evaluation at the end of the five-year period2. Such evaluations
provide mechanisms for accountability and progress monitoring. Therefore, we assume that climate
targets clearly stated in a Five-Year Plan are more actionable and credible due to their integration into
performance evaluations, alignment with broader development priorities, and pragmatic approach to
implementation.

• Time normalised progress index (TNPI): With China’s 14th Five-Year Plan nearing completion, evaluating
the extent to which each target is on track to be achieved could provide a foundation for developing
new policies, plans, and targets for the next five years. Given the uncertainties in tracking broad goals
like carbon neutrality and peak emissions, as well as the need for further model quantification, our
assessment focuses exclusively on quantifiable, trackable, and statistically measurable targets (see SI
Table 3). All evidence sources on progress are provided in the supplementary data.

For each target, we first review and calculate raw progress as the proportion of the indicator gap that has been
closed by the current year relative to the base year:

𝑹𝒂𝒘 𝒑𝒓𝒐𝒈𝒓𝒆𝒔𝒔 = (𝑪 𝒗− 𝑩 𝒗) / (𝑻 𝒗− 𝑩 𝒗) (1)

Where 𝐵 , 𝑇 , 𝐶 are the base, target, and current values of the indicator. Targets where raw progress ≥100%
𝑣 𝑣 𝑣
are classified as Achieved. To ensure comparability across targets with different deadlines, we assume policy
indicators advance along a linear trajectory and normalise progress by the proportion of time elapsed. This
allows both short-and long-term goals to be evaluated based on whether progress is keeping on track with time
passed. The resulting time-normalised progress index (TNPI) is given by:

𝑻𝑵𝑷𝑰 = ((𝑪 𝒗−𝑩𝒗)/(𝑻𝒗−𝑩𝒗)) / ((𝑪 𝒚−𝑩𝒚)/(𝑻𝒚−𝑩𝒚)) ∗𝟏𝟎𝟎% (2)

Where 𝐵 , 𝑇 , 𝐶 are the base, target, and current years. TNPI ≥100% indicate the target is on or ahead of
𝑦 𝑦 𝑦
schedule; TNPI <100% indicate it is lagging. For infrastructure-related targets with long lead times (e.g., nuclear
power, hydropower), we use operational and under-construction capacity as the current value to better account
for construction lead times. All progress data are primarily based on official announcements and news releases
from the Chinese government. When such information is unavailable, we make simple estimates using data from
the IEA and public reports (e.g., Reuters). The specific data sources for each target’s progress are listed in
supplementary data.

For targets still in progress (raw progress < 1), we classify them using a weighted credibility score. Each target’s
score combines three components: governance level, inclusion in the 14th Five-Year Plan, and the TNPI. Figure 3.
(b) presents the overall credibility score using weights where 𝜔 :𝜔 :𝜔 = 3:2:5. Targets are then
𝑔𝑜𝑣 𝐹𝑌𝑃 𝑇𝑁𝑃𝐼
clustered into categories as follows: High credible (score ≥ 67th percentile), Medium credible (33rd–67th
percentile), and Low credible (below 33rd percentile). To evaluate the sensitivity of credibility scores to
weighting scheme and ensure the robustness of the clustering, we tested five alternative weighting schemes
(see SI Section 1). Across weighting schemes, target classifications are largely robust. Only two targets shift
category when the TNPI weight is lower than the other two indicators (see SI Table 13). We therefore adopt the
consistent clustering results from the first four weighting schemes for our subsequent scenario quantification
and report an additional weighting-related sensitivity case in SI Section 6.1. SI Figure 5 and 6 indicate that
weighting-related sensitivity does not undermine our findings on China’s energy transition and its emission
implications.

In China’s updated NDC, the targets for non-fossil energy share and installed wind and solar capacity extend
existing pledges and are incorporated into our policy credibility assessment. However, the new GHG emissions
target is China’s first absolute reduction pledge, with no historical benchmark for evaluation; It is intended to be
based on the peak year; however, since it remains unclear whether emissions have already peaked, we treat it as
a policy sensitivity case (see SI Section 6.2).

Scenario development with morphological approach
In addition to lacking an exploratory perspective on policy credibility, many scenario studies of China adopt
uniform socioeconomic assumptions drawn from global Shared Socioeconomic Pathways (SSPs)11,35, or conduct
additional sensitivity analyses only post hoc12. Such practices implicitly assume that socioeconomic development
evolves independently of climate policy, neglecting the Chinese government’s pivotal role in orchestrating
economic transformation while managing trade-offs with its decarbonisation agenda.

The difference in climate policy implementation we assess reflect, through bespoke scenario narratives, the
complex interactions among economic priorities, technological capabilities, governance capacity, and public
engagement. Capturing this complexity requires a more flexible and context-specific approach to scenario
design. Morphological analysis is the method used to visualise and analyse the complex network of relationships
across the socio-technical system36 (See Figure 4.(a)) . A key advantage of this method is its transparency in
combining multiple system drivers into a limited number of internally coherent scenarios. Morphological
analysis is particularly well-suited to doing this, allowing for assessment of real-world decarbonisation pathways
under uncertainty related to governance and policy implementation in the Chinese context.

Our scenario design draws directly from official policy discourse and is anchored in China’s current development
challenges and structural constraints (SI Table 4). In response to mounting pressures from economic slowdown,
the Chinese government has proposed a suite of high-level strategies to shape future national development. The
resulting internally consistent scenarios serve two purposes: first, as plausible pathways for China's coordinated
evolution across economic, demographic, technological, and policy dimensions; and second, as a structured
exploration of how existing development strategies might interact under current socio-economic conditions to
guide future trajectories. Together, these narratives offer a more context-specific framing for scenario modelling
in China, bridging real-world policy signals with energy system analysis. For this study, four China-specific
scenarios were developed. Here, we present a summary of each scenario. A comparative overview of key
metrics across all four scenarios is reported in Figure 4. (b). For more details on the scenario development
process and narratives, including the national strategies referenced, please see SI Section 2 and 3.

Green Lights (GL): Green Lights depicts a future where China achieves high economic growth and climate
leadership by 2060, reaching net-zero emissions by 2050. Through strategies like Dual Circulation 37 and the
Beautiful China Initiative38, the economy transitions to green, high-tech industries. Strong government action
and falling technology costs drive electrification, innovation, and widespread public engagement. Clean tech
deployment and carbon removal technologies, such as CCS and DAC, scale rapidly. Sustainable lifestyles gain
traction, though fertility rates continue to decline. GL illustrates how climate ambition and economic prosperity
can align, positioning China as a global model for low-carbon development.

Calm Sea (CS): Calm Sea presents a balanced pathway where China maintains moderate economic growth while
fully implementing current climate policies. The “new three” industries—solar, EVs, and batteries—drive
decarbonisation and economic stability. China peaks emissions by 2030 and achieves carbon neutrality by 2060
through steady progress. Clean energy costs decline, enabling broad adoption, while CCS and DAC technologies
scale moderately. Public behavioural change is policy-driven rather than voluntary. Economic reforms like Dual
Circulation help avoid deeper downturns, but the lack of bold policies limits transformative change. CS reflects a
pragmatic path, with policy stability supporting gradual decarbonisation.

Red Sun (RS): Red Sun envisions a vibrant economy driven by real estate recovery39 and urbanisation40, with
climate goals taking a back seat. China meets its 2030 NDC but struggles to reach net zero by 2060. Policy
support focuses on economic growth, while industrial and carbon removal transitions are limited. Fertility rates
stabilize due to social reforms and rising confidence. Consumerism and comfort take priority, with green tech
adopted more for convenience than sustainability. Though emissions fall slightly through incremental technology
uptake, weak climate governance and limited public engagement prevent China from making transformative
progress.

Great Wall (GW): Great Wall reflects a constrained future marked by economic stagnation, declining population,
and stalled climate progress. High debt, a sluggish real estate sector, and weak consumption hinder economy
reform. Technological innovation slows down, and clean energy deployment plateaus. CCS and DAC remain in
early demonstration stages, and pro-natalist policies fail to halt population decline. Rising economic insecurity
limits the government’s capacity for sustained climate actions. China falls short of its 2025 climate targets and
struggles to meet its 2030 NDC and 2060 net-zero goals. GW underscores the risks of limited government action
and delayed response to climate and development challenges.

Policy quantification across Chinese energy transition scenarios
Policy quantification is guided by the assessed credibility of individual targets—classified as Achieved, High,
Medium, or Low; and by scenario narratives developed using a morphological approach. The Great Wall
scenario, which enforces only Achieved targets, therefore represents the most conservative pathway, reflecting
minimal policy delivery. For targets that have already been achieved or overachieved, we implement the realised
levels observed by 2025 rather than the stated target values, in order to more accurately capture China’s existing
climate efforts. Beyond the target year, these achieved levels are assumed to be maintained and form a
minimum baseline from 2025 onwards.

Across scenarios, policy implementation expands progressively from higher- to lower-credibility targets. Moving
from Great Wall to Green Lights, the assumed strength of policy delivery increases, with an expanding set of
targets implemented, ultimately culminating in the full realisation of China’s overarching climate commitment:
carbon neutrality by 2060 or earlier (assumed as 2050). Detailed mappings between specific targets and
scenarios are provided in SI Table 6.

Global climate scenarios integrating Chinese uncertainty
To explore China’s role in global climate mitigation, we model the above four China-specific scenarios under two
potential levels of global climate action for the Rest of the World: Nationally Determined Contributions (NDC)
and Long-Term Strategies (LTS) (See Table 1). Based on this approach, we can understand the implications of
Chinese policy action in two different global contexts, one where the international community achieves limited
ambition, based on NDCs, and another where stronger action is achieved, consistent with a 2°C target. Although
in reality China’s climate actions would directly and indirectly influence other regions’ ambition levels and global
responses are unlikely to be entirely independent, this study focuses on isolating the global boundary effects of
China’s uncertainty. Therefore, we do not model dependencies or geopolitical interactions between China and
the rest of the world.

First, an NDC case was developed based on Meinshausen et al.41. This study provides GHG emissions targets
derived from NDCs for 2025 and 2030 for 196 countries as of November 2021, following the completion of
COP26. It also includes emissions pledges for international aviation and shipping at the global level for these
years. The NDC targets are aggregated to the TIAM-UCL regions, while international transport emissions
(aviation and shipping) are distributed to the regions using their respective shares of these emissions in 2020,
according to IEA data.

To extend NDC ambition beyond 2030, we follow van de Ven et al., assuming each region’s 2020–2030 emissions
intensity reduction rate continues through the century42. To implement this, we use GDP projections from
Shared Socioeconomic Pathway 2 (SSP2). Additionally, for this climate policy cases, we incorporate land-use,
land-use change, and forestry (LULUCF) emissions and non-CO2 emissions (CH₄ and N₂O) based on pathways
already included in the TIAM-UCL model which draw on an average of four SSP2-RCP scenarios from the IMAGE,
MESSAGE, REMIND and WITCH models43 : the NDC case uses SSP2-RCP 6.0 scenario while the Net Zero case
follows the SSP2-RCP 2.6 pathway. To better capture China-specific afforestation dynamics, and reflect official
forestation targets, we adopt China’s LULUCF trajectory based on He et al. which estimates the future carbon
future carbon-removal potential consistent with national forestation targets44.

Second, we represent more ambitious climate action in other regions by incorporating various net-zero
pledges42. These pledges are applied to the regions listed in Table 2a, which covers countries and regions with
explicit net-zero targets. For regions where national commitments vary widely (Table 2.(b)), we assume that each
region partially achieves net zero based on the emission share of countries within it that have adopted net-zero
targets, and all these regions converge to net-zero GHG emissions by 2100, even if some countries have not yet
adopted net-zero targets. In the modelling, we aggregate country pledges and report the share of 2020 regional
GHG emissions covered by net-zero commitments by 2050, 2060, and 2070, and implement this proportion as
GHG emissions reduction relative to the NDC baseline case. This approach reflects the ambition of major
emitters within each region while avoiding over-reliance on early pledges from smaller countries.

The TIAM-UCL model
To explore the implementation of our China-focused policy scenarios and their global implications, we use the
TIMES Integrated Assessment Model at University College London (TIAM-UCL). This model provides a
representation of the global energy system, capturing primary energy sources (oil, fossil methane gas, coal,
nuclear, biomass, and renewables) from production through to their conversion (electricity production,
hydrogen and biofuel production, oil refining), their transport and distribution, and their eventual use to meet
energy service demands across a range of economic sectors. Using a scenario-based approach, the evolution of
the system over time to meet future energy service demands can be simulated, driven by a least-cost objective.
The model uses the TIMES model framework.

The model represents the countries of the world as 16 regions, enabling detailed characterisation of energy
systems in each region and the trade flows between regions. Regional coal, oil and gas prices are generated
within the model. These incorporate the marginal cost of production, scarcity rents (e.g. the benefit foregone by
using a resource now as opposed to in the future, assuming discount rates), rents arising from other imposed
constraints (e.g. depletion rates) and transportation costs, but not fiscal regimes. This means full price
formation, which includes taxes and subsidies, is not captured in TIAM-UCL, and remains a contested limitation
of this type of model45.

The model has a limited number of technological options for carbon dioxide removal (CDR) from the
atmosphere, including a set of bioenergy with carbon capture and storage (BECCS) technologies, in power
generation, industry, and in hydrogen and biofuel production. The climate module used in the model enables
the translation of greenhouse gas emissions from the energy system into atmospheric concentrations, radiative
forcing and temperature change. The module is mainly based on that developed by ETSAP46 , but has been
subsequently recalibrated to the MAGICC model results47. Further details on recent model developments can
be found in Pye et al. 48

Future demands for energy services (including mobility, lighting, residential, commercial and industrial heat and
cooling) are exogenously defined and drive the evolution of the system so that energy supply meets demand
throughout the time horizon. Projections of China’s energy service demand are based on GDP and population
assumptions specific to each scenario (SI Figure 4), aligning with narratives of China’s economic transition (SI
Section 4). Energy service demands for the rest of the world are derived from SSP2. Decisions around what
energy sector investments to make across regions are determined based on the cost-effectiveness of
investments, taking into account the existing system today, energy resource potential, technology availability,
and policy constraints.

TIAM-UCL runs in 5-year steps from 2005 to 2100, calibrated to IEA data and updated for 2010–2020 with
additional constraints introduced to represent existing system today. The social discount rate used in the
calculation of net present value (used as the basis for the objective function) is set at 3.5%. Further information
on key assumptions used in the model is provided in SI section 4. The TIAM-UCL model version used for this
analysis was 4.1.2 and was run using TIMES code 4.5.6 with GAMS 38. The model solver used was CPLEX
20.1.0.1.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study combines an energy system optimization model with a policy credibility-based scenario framework to evaluate the credibility of China’s climate policy targets and assess the implications of different credible policy packages for future emissions pathways and cumulative emissions in China and globally. I find the overall research design to be clever and the use of the UCL-TIAM model appropriate and well-established in the literature. However, I believe there is a fundamental and potentially unresolvable issue with the policy credibility metric that significantly undermines the validity of the main conclusions. Below I explain why this issue is critical and how it affects the interpretation of the results.\nIn the Methods section (Lines 329–330), the authors construct a composite policy credibility indicator comprising three dimensions: (1) administrative level of the policy target (higher levels score higher), (2) whether the target is included in China’s Five-Year Plan (included targets score higher), and (3) the level of progress toward the target. The third dimension is quantified as the ratio of current progress to expected progress, i.e., (current – base)/(target – base). A lower ratio indicates lower credibility.\nThis metric, however, suffers from two critical flaws:\nFirst, it assesses indicator progress, not policy progress. In practice, indicators often lag behind policy implementation, especially for infrastructure-related targets. For example, the authors assess the credibility of China’s nuclear power target as very low (see Methods Figure b and SI), based on the gap between current installed capacity in 2023 and the 70 GW target for 2025. However, due to the long lead time of nuclear construction (6–7 years), many projects initiated during the 14th Five-Year Plan will only come online by 2025, and their progress is essentially certain. A more appropriate indicator would be installed + under-construction capacity or projected 2025 operational capacity. Using these metrics would yield significantly higher credibility scores for the nuclear target.\nSecond, the indicator is highly sensitive to the time horizon of the target. For instance, the authors assess China’s non-fossil energy share targets for 2025, 2030, and 2060. Because the numerator (current – base) is constant across targets, longer-term targets with larger denominators result in lower credibility scores. This artificially favors short-term targets as more “credible” and penalizes long-term ones. As a result, the scenario grouping by credibility is effectively a grouping by time horizon. The conclusions are thus driven by whether China meets its long-term targets, not whether the targets are credible per se. This weakens both the policy relevance and academic contribution of the study.\nI am not sure whether this issue can be resolved without revisiting the scenario design, but I believe it has serious implications that challenge the main conclusions of the paper.\nIn addition, I offer the following specific comments:\n1.The paper does not include scenarios or discussion on carbon sinks, which are an essential component of China’s carbon neutrality target but not of the peaking target. The credibility assessment should include carbon sink-related targets where applicable.\n2.In Methods Figure b, I recommend using different colors to distinguish policy targets by sector to improve readability.\n3.In Figure 2, panels a and b end at 2060, while panels d and f report cumulative emissions and warming until 2100. The emissions pathways in a and b should be extended to 2100 for consistency.\n4.Since official targets only extend to 2060, all post-2060 assumptions (such as maintaining emissions decline rates) are hypothetical. These assumptions appear to be the dominant driver of the differences in cumulative emissions by 2100. For example, in Figure 3c, the difference in cumulative emissions by 2050 is less than 100 GtCO₂, whereas the difference from 2050 to 2100 exceeds 400 GtCO₂. The authors’ claim in the abstract that “uncertainty in the delivery of China’s domestic policy alone could lead to a difference in cumulative global emission reductions of up to ~600 GtCO₂ by 2100, equivalent to a ~0.2 °C warming difference” appears to significantly overstate the role of policy credibility.\n5.In Figure 2b, the GW and RS scenarios suggest China’s power sector is nearly decarbonized by 2060. Yet Figure 2c shows continued coal consumption in the power sector under those same scenarios (purple bar). This inconsistency needs to be clarified.\n6.Figure 2 indicates that inter-scenario differences are largely driven by the extent of industrial electrification and coal reduction, rather than differences in the power sector. However, the conclusion emphasizes coal phaseout in power generation as the key. This is not well supported by the results.\n7.The Discussion section lacks close integration with the results. The three key messages listed in the discussion are not clearly linked to the earlier findings and should be revised for consistency.\n8.The descriptions of the four scenarios are only found in the Methods section. A brief summary of each scenario in the main text would greatly improve accessibility and comprehension for readers.", "reviewer_2": "This review addresses the manuscript titled “The global implications of uncertainty in China's climate policy delivery,” submitted to Nature Communications. The paper aims to quantify the effect of uncertainty in China’s climate policy delivery on global emissions and temperature outcomes. It addresses the following questions: (1) How credible are China’s climate and energy policies? (i.e. how likely is it that the targets they set will be achieved? (2) Taking into account the uncertainty stemming from varying credibility, what are the range of emissions outcomes for China over the course of the century? (3) What are the implications of this range for global temperature outcomes under two different scenarios for global ambition?\nThe paper is both innovative and policy relevant. It makes three main contributions. First, it adapts a generic framework for assessing policy credibility to the specific institutional and political context of China, providing a methodological advance. Second, it develops a novel dataset that transparently evaluates the credibility of China’s climate and energy targets. Third, it quantifies the substantial implications of uncertainty in China’s policy delivery for both emissions trajectories and global temperature outcomes.\nAddressing several issues would further strengthen the paper:\nA first issue concerns the scope of targets assessed. The paper states that 289 total targets were identified, of which 87 were quantifiable in TIAM, but fewer than 50 were ultimately assessed for credibility. It would be helpful to clarify the difference between the 87 and the <50 – is the difference due entirely to local/regional targets? More critically, how do the excluded targets factor into the scenarios, if at all? Are they assumed to have no impact, and if so, is that assumption defensible? Since the excluded targets are unlikely to be random (for instance, energy-sector targets are more readily quantifiable in TIAM, whereas land-use or efficiency targets may be less so), the criteria for inclusion or exclusion could introduce bias. It would be helpful to clarify the rationale for this filtering step and discuss how it might affect the results.\nA second issue relates to the credibility assessment methodology. The framework and the choice of indicators (governance level, inclusion in the Five-Year Plan, and progress to date) are logical. There is a missed opportunity, however, to engage with literature discussing factors in the target implementation gap (e.g. 1, 2), which could provide a stronger foundation for the indicators chosen. Additionally, while the rationale for weighting progress to date higher than the other two indicators is clear, the weightings in general seem somewhat arbitrary. How sensitive are the results to the weightings chosen?\nA third issue concerns the treatment of targets after their stated target year. While the paper is clear about which targets are assumed to be achieved in which scenarios, it is less clear how this achievement is translated into the modeling framework, and in particular what was assumed about progress on the target indicators following the target year. There are two potentially conflicting logics: On the one hand, one could interpret an “achieved” target as evidence of strong institutional capacity and assume continued progress thereafter. Alternatively, an “achieved” target could reflect an unambitious goal, which might correlate with weak implementation capacity, in which case it would not signal a trajectory of sustained improvement. I was not able to decipher how such logic is carried forward from the credibility assessment into the scenario narratives and ultimately into the IAM results. Greater clarity on this point would be valuable.\nA fourth set of issues relates to the global net zero scenario. The accuracy of assumptions in Table 2 should be confirmed. For instance, while emission coverage for Central and South America is said to cover CO2 only, the net-zero targets of the largest countries in the region (e.g. Brazil, Argentina, Chile, Peru, Colombia) cover all GHGs (3, 4). Likewise for the former Soviet Union. The basis for choosing a “representative” country for certain regions should also be clarified – are these countries representative in terms of size of economy, emissions profile, or some other indicator? (For example, on what basis is Nigeria said to be representative of Africa?) Alternatively, it is plausible that the results are not particularly sensitive to these assumptions, in which case, it would be helpful to clarify that.\nReferences\n1. L. Peterson, H. and van Asselt, Assessing risks to the implementation of NDCs under the Paris Agreement. Clim. Policy 0, 1–15.\n2. T. Fransen, J. Meckling, A. Stünzi, T. S. Schmidt, F. Egli, N. Schmid, C. Beaton, Taking stock of the implementation gap in climate policy. Nat. Clim. Change, 1–4 (2023).\n3. Climate Watch, Net-Zero Tracker (2024). https://www.climatewatchdata.org/net-zero-tracker.\n4. Energy and Climate Intelligence Unit, Data Driven Envirolab, New Climate Institute, University of Oxford, Net Zero Tracker, (2024); https://zerotracker.net/."}, "author_response": "Responses to Reviewers\nNCOMMS-25-41170-T\nOctober 2025\nReviewer 1\nGeneral comments:\nThis study combines an energy system optimization model with a policy credibility-based\nscenario framework to evaluate the credibility of China’s climate policy targets and assess\nthe implications of different credible policy packages for future emissions pathways and\ncumulative emissions in China and globally. I find the overall research design to be clever\nand the use of the UCL-TIAM model appropriate and well-established in the literature.\nHowever, I believe there is a fundamental and potentially unresolvable issue with the policy\ncredibility metric that significantly undermines the validity of the main conclusions. Below I\nexplain why this issue is critical and how it affects the interpretation of the results.\nIn the Methods section (Lines 329–330), the authors construct a composite policy credibility\nindicator comprising three dimensions: (1) administrative level of the policy target (higher\nlevels score higher), (2) whether the target is included in China’s Five-Year Plan (included\ntargets score higher), and (3) the level of progress toward the target. The third dimension is\nquantified as the ratio of current progress to expected progress, i.e., (current – base)/(target\n– base). A lower ratio indicates lower credibility.\nThis metric, however, suffers from two critical flaws:\nFirst, it assesses indicator progress, not policy progress. In practice, indicators often lag\nbehind policy implementation, especially for infrastructure-related targets. For example, the\nauthors assess the credibility of China’s nuclear power target as very low (see Methods\nFigure b and SI), based on the gap between current installed capacity in 2023 and the 70 GW\ntarget for 2025. However, due to the long lead time of nuclear construction (6–7 years),\nmany projects initiated during the 14th Five-Year Plan will only come online by 2025, and\ntheir progress is essentially certain. A more appropriate indicator would be installed +\nunder-construction capacity or projected 2025 operational capacity. Using these metrics\nwould yield significantly higher credibility scores for the nuclear target.\nSecond, the indicator is highly sensitive to the time horizon of the target. For instance, the\nauthors assess China’s non-fossil energy share targets for 2025, 2030, and 2060. Because\nthe numerator (current – base) is constant across targets, longer-term targets with larger\ndenominators result in lower credibility scores. This artificially favors short-term targets as\nmore “credible” and penalizes long-term ones. As a result, the scenario grouping by\ncredibility is effectively a grouping by time horizon. The conclusions are thus driven by\nwhether China meets its long-term targets, not whether the targets are credible per se. This\nweakens both the policy relevance and academic contribution of the study.\nI am not sure whether this issue can be resolved without revisiting the scenario design, but I\nbelieve it has serious implications that challenge the main conclusions of the paper.\nResponse:\nThank you for the constructive comments, which highlight two limitations in our initial credibility\nassessment approach. In this revision, we have updated the progress review of all targets involved,\ntaking account power plant capacity under construction for relevant targets, and adjusted the\nprogress indicator to capture the potential time horizon issues.\nOur responses to this methodological issue are organised around the following three points:\n1. Policy progress versus indicator progress\nIn this study, our objective is not to evaluate policy progress per se, but rather policy credibility. The\nprogress index is one of the metrics used to assess whether a policy indicator is on track, while the\nother two dimensions, governance level and inclusion in the Five-Year Plan, reflect the authority and\ninstitutional strength of the policy. We acknowledge that some targets may show limited progress to\ndate yet still be considered credible, particularly those with mid-term timelines. This is why we\nadopted a weighting approach in which governance levels assigns additional credibility to mid- or\nlong-term targets. However, we recognise that our initial weighting method involved a degree of\narbitrariness. Therefore, in this revision, we introduce five alternative weighting schemes, including\none in which the progress indicator carries a lower weight relative to the other two. This new\nconfiguration generates a sensitivity case, which is discussed in detail in our response to Reviewer\n2’s comment 2.\n2. Infrastructure-related indicator progress lag\nThank you for pointing out the issue of construction lead time in progress assessment. We agree that\nthis construction lead time issue is particularly relevant for infrastructure/technology-related\nindicators, though it is less significant across other targets. Therefore, in this revision, we now\naccount for both operational capacity and under-construction capacity of nuclear when evaluating\nsuch indicators, which better reflects policy implementation progress and delivery certainty. Similar\nadjustments have been applied to hydropower targets.\n3. Sensitivity of credibility scores to time horizon\nWe acknowledge the reviewer’s concern that our original metric may be sensitive to target time\nhorizons, but we do not agree with the view that this introduces significant bias and challenges our\nconclusions. First, as noted earlier, each target’s credibility score comprises three components. Long-\nterm targets generally have higher governance levels and are therefore not artificially penalised.\nSecond, our scenario allocation does not simply follow year targe, that is nearer term targets are not\nnecessarily more credible. For example, several 2030 targets are already implemented in Red Sun\ndue to their high credibility (e.g., EV, solar, and wind targets), while some 2025 industrial targets are\nonly realised in Green Lights because of their lower credibility. In this sense, targets are not grouped\ninto scenarios by time horizon. Third, while we constructed four distinct China-specific scenarios to\nexplore potential variations in its energy transition, our key conclusions on its global implications are\nderived from the differences between these scenarios. The critical boundary lies between Great Wall\n(which includes only achieved or overachieved targets) and Green Lights (which implements all\npolicies issued). Thus, the main findings regarding China’s policy delivery and its global implications\nremain robust.\nHowever, we do recognise the benefits of improving comparability across targets with different\ndeadlines and reducing uncertainty in policy assessment. To address this limitation, we made two\nkey revisions:\n• Updated progress review: The earlier version of the manuscript assessed target progress as\nof July 2024. We now provide a comprehensive update as of 28 September 2025,\nincorporating the latest government press releases, statistical bulletins, and industry reports.\nThis ensures that short-term (2025) targets are evaluated against the most up-to-date\nevidence of implementation.\n• Time-normalised progress index (TNPI): We adjust our raw progress index to time normalised\nprogress index (TNPI). TNPI is calculated as Raw progress/ Time fraction, where Raw\nprogress = (current – base) / (target – base), and Time fraction = (current year – base year) /\n(target year – base year). TNPI compares the share of the target gap closed with the share of\ntime elapsed. This adjusted indicator is clarified to assess if targets with different deadlines\nare on track. For example, while the raw progress toward the 2060 non-fossil share target is\nonly 3.5% by 2023, the TNPI indicates 45.3% when normalised for time. However, this\ntreatment includes an implicit assumption that policy indicators advance along a linear\ntrajectory; we have clarified this assumption in the manuscript.\nTogether, these revisions strengthen the robustness of our credibility assessment. The relevant\nrevisions are reflected in the manuscript (lines 339-376) and in the Supplementary Information\n(lines 71–159). All updated data sources, calculations, and the revised credibility scores are\nprovided in the SI_PolicyCredibilityAssessment.xlsx\nComment 1.1\nThe paper does not include scenarios or discussion on carbon sinks, which are an essential\ncomponent of China’s carbon neutrality target but not of the peaking target. The credibility\nassessment should include carbon sink-related targets where applicable.\nResponse:\nThank you for highlighting the importance of carbon sinks in the context of China’s carbon neutrality\ntarget. Both engineered and natural sinks are considered in our scenario framework; however, we\nrecognise the limitations of our previous treatment and have made corresponding enhancements in\nthis revision.\nFor engineered sinks (CCS/BECCS/DAC), while China currently lacks a quantified national deployment\ntarget, several demonstration and build-out plans exist. Accordingly, we did not include a quantified\npolicy-based CCS target in the credibility assessment, but we do incorporate CCS deployment\nuncertainty as a scenario dimension across all scenarios. The CCS assumptions, derived from China’s\nofficial CCUS roadmap and a systematic review of deployment potential, are detailed in the SI\nsection 4.5.\nFor natural sinks (LULUCF), the TIAM-UCL model represents CO₂ emissions from land use, land-use\nchange, and forestry at the regional level, using exogenous inputs from other integrated assessment\nmodels. In our NDC scenarios, we adopt the average LULUCF trajectory from four SSP2–RCP6\npathways (IMAGE, MESSAGE, REMIND, and WITCH). For the net-zero scenarios, we use a global\nLULUCF trajectory consistent with SSP2–RCP2.6 (IMAGE), which delivers net-negative CO₂ emissions\nfrom 2060 onward.\nHowever, we recognise that this generic treatment does not fully capture China-specific\nafforestation dynamics and is not closely aligned with China’s official forestation targets:\n• By 2025, the forest coverage rate will reach 24.1%, and the forest stock volume 18 billion m³.\n• By 2030, the forest coverage rate will reach about 25%, and the forest stock volume 19\nbillion m³.\nGiven the structural limits of TIAM’s land-use sector, it is challenging to represent forest coverage or\nstock volume directly. In this revision, we therefore parameterise China’s LULUCF trajectory based\non He et al. (2024), who implemented OSCAR-China to estimate future carbon-removal potential\nconsistent with national forestation targets. Their study shows that, if these targets are achieved and\nextended, China’s land sinks could reach −0.35 ± 0.04 Gt C yr⁻¹ by 20601.\nOur updated policy assessment indicates that China has already exceeded its near-term forestation\ngoals. Accordingly, in the revised modelling, these targets are classified as “ACHIEVED” and are\nimplemented in the Great Wall scenario and across all of the other more ambitious scenario.\nAdditional clarifications and corresponding revisions have been incorporated in the manuscript (lines\n478-485).\nComment 1.2\nIn Methods Figure b, I recommend using different colours to distinguish policy targets by\nsector to improve readability.\nResponse:\nThank you for this helpful suggestion. In the revised manuscript, we have expanded the policy\ncredibility assessment to include additional targets (e.g., forest stock and the 2035 NDC), with\nprogress updated to the most recent evidence (September 2025). To improve readability, we now\nuse different colours and text labels to distinguish policy targets by sector, following the same colour\nscheme as in the results figures. Given some of the targets that have been substantially\noverachieved (such as forest stock and wind/solar capacity), we have also adopted a broken-axis\nfigure to better illustrate the large variation in progress across targets. This approach improves\nreadability and more clearly reflects both China’s significant achievements and areas where progress\nlags. The revised figure is included in the manuscript.\nFigure 3.b Credibility assessment of China’s climate and energy goals across various sectors\n(with details of each target assessment provided in SI section 1).\nComment 1.3\nIn Figure 2, panels a and b end at 2060, while panels d and f report cumulative emissions\nand warming until 2100. The emissions pathways in a and b should be extended to 2100 for\nconsistency.\nResponse:\nThank you for this helpful suggestion. In the revised manuscript, we have extended the emissions\npathways in Figure 2 panels a and b to 2100, ensuring consistency with panels d and f. The changes\nin the net-zero group for the rest of the world result from updated net-zero assumptions for key\ndeveloping regions, as detailed in our response to Reviewer 2, Comment 2.4.\nComment 1.4\nSince official targets only extend to 2060, all post-2060 assumptions (such as maintaining\nemissions decline rates) are hypothetical. These assumptions appear to be the dominant\ndriver of the differences in cumulative emissions by 2100. For example, in Figure 3c, the\ndifference in cumulative emissions by 2050 is less than 100 GtCO₂, whereas the difference\nfrom 2050 to 2100 exceeds 400 GtCO₂. The authors’ claim in the abstract that “uncertainty\nin the delivery of China’s domestic policy alone could lead to a difference in cumulative\nglobal emission reductions of up to ~600 GtCO₂ by 2100, equivalent to a ~0.2 °C warming\ndifference” appears to significantly overstate the role of policy credibility.\nResponse:\nThank you for raising this concern regarding the influence of post-2060 assumptions on China’s\npolicy impact. We acknowledge that these assumptions inevitably affect the absolute values of\ncumulative emissions and temperature outcomes by 2100. However, our analysis focuses on\nthe differences arising from variations in China’s policy delivery and socio-economic–technological\ndevelopment (the other four dimensions in our morphological analysis), rather than on the absolute\nlevels themselves.\nThis study evaluates policies issued up to 2025, excluding potential future policies that have not yet\nbeen announced, as policy projection lies beyond the scope of our research. Our analysis assumes\nthat policy ambition is sustained, at minimum, at current levels of effort (Great Wall). Our findings\ntherefore represent a scenario-based assessment rather than a prediction or forecast. The long-term\noutcomes up to 2100 reflect the implications of existing policies and foreseeable socio-economic\nconditions (long-term assumptions about economic, demographic, and technological trends are\nclarified in SI Section 4). This is precisely what we aim to evaluate. Meanwhile, the differences\nshown in Figure 3c, approximately 75 GtCO₂ by 2050 and 500 GtCO₂ by 2100 (new modelling\nresults), remain robust under two distinct global contexts (NDC and Net Zero). We therefore\nconsider our conclusions regarding China’s policy delivery implications to be robust within our\nscenario and modelling framework.\nThe slight reduction compared with our previous analysis (from ~600 GtCO₂ to ~500 GtCO₂, and from\n0.20 °C to 0.17 °C of warming difference) arises from three key updates:\n• Updated policy progress: We reassessed all policy targets using the most recent statistical\ndata (as of September 28, 2025; the previous assessment was based on data up to July\n2024), which show that a greater number of targets have now been achieved. The new\nassessment also updates the planned values of overachieved targets with actual progress\ndata. Together, these revisions enhance and better reflect the mitigation potential of the\nmost conservative (Great Wall) scenario.\n• Inclusion of quantified forestation targets: the incorporation of updated, quantified\nforestation targets further enhances the mitigation potential across all scenarios. As these\ntargets are assessed as achieved, their inclusion provides additional representation of land-\nbased carbon removal within the model.\n• Inclusion of 2035 NDC targets of non-fossil share and wind and solar capacity instalment.\nComment 1.5\nIn Figure 2b, the GW and RS scenarios suggest China’s power sector is nearly decarbonized\nby 2060. Yet Figure 2c shows continued coal consumption in the power sector under those\nsame scenarios (purple bar). This inconsistency needs to be clarified.\nResponse:\nThank you for the comment. Figure 2b presents the breakdown of electricity generation sources,\nwhile Figure 2c shows coal consumption for power sector include both electricity and heat\ngeneration. In the TIAM model, the power sector includes both electricity-generation technologies\nand heat-producing technologies (Public heat generation plants, providing heat to local networks),\nwhich explains the distinction between these two figures. Further details on the power sector\nconfiguration can be found in the TIAM documentation, Section 4.1 Power Generation2 .\nIn this revision, we separated heat production from Power and represented it in light purple in\nFigure 2. Additional clarification has been added to the figure caption (lines 175-177).\nComment 1.6\nFigure 2 indicates that inter-scenario differences are largely driven by the extent of\nindustrial electrification and coal reduction, rather than differences in the power sector.\nHowever, the conclusion emphasizes coal phaseout in power generation as the key. This is\nnot well supported by the results.\nResponse:\nThank you very much for this insightful comment. In this revision, we have updated our policy\ncredibility assessment to capture the most recent progress on numerical targets and to include\nprojected progress in nuclear and hydropower installed capacity. As a result, more targets are now\ncategorised as achieved, reflecting the strengthened policy efforts in the GW and RS scenarios.\nThe revised results show more clearly that existing policy efforts ensure high certainty in the\nelectrification expansion and power sector transition, while also highlighting the key role of\nindustrial electrification and coal reduction which also drives inter-scenario differences. Accordingly,\nwe have re-analysed the modelling results and revised the Discussion section to better align our\nconclusions with these updated modelling findings.\nComment 1.7\nThe Discussion section lacks close integration with the results. The three key messages listed\nin the discussion are not clearly linked to the earlier findings and should be revised for\nconsistency.\nResponse:\nThank you for this comment. As a study combining qualitative and quantitative approaches, the\nDiscussion section is designed to go beyond the numerical modelling results and to summarise\nbroader insights from our morphological scenario analysis (a systemic assessment of China’s\neconomy, society, technology, and policy context) as well as from our policy review and assessment,\nincluding elements not directly quantified in the modelling framework. Given the methodological\nchanges in policy credibility assessment and the updated net-zero assumptions for the rest of the\nworld, we reran our scenarios and revised the Discussion to integrate the new findings(See lines 230-\n269). We believe this revision enhances consistency with the quantitative results and qualitative\ninsights.\nComment 1.8\nThe descriptions of the four scenarios are only found in the Methods section. A brief\nsummary of each scenario in the main text would greatly improve accessibility and\ncomprehension for readers.\nResponse:\nThank you for your helpful suggestion. We have added a brief summary of the four scenarios in the\nmain text to clarify their differences and improve the readability for readers.\nManuscript line 60-65:\n“The scenarios capture a diverse set of futures for China’s climate governance, alongside corresponding\nshifts in socioeconomic and technological development (see Methods and SI section 2). Great Wall (GW)\nreflects a constrained future marked by economic stagnation, declining population, and stalled climate\nprogress. Red Sun (RS) envisions strong economic growth, driven by real estate market recovery and\nurbanisation, while climate goals are deprioritised. Calm Sea (CS) presents a balanced pathway where\nChina maintains moderate economic growth while fully implementing current climate policies. Green\nLights (GL) depicts a future where China demonstrates climate leadership while maintaining high\neconomic growth.”\nReviewer 2\nGeneral comments\nThis review addresses the manuscript titled “The global implications of uncertainty in\nChina's climate policy delivery,” submitted to Nature Communications. The paper aims to\nquantify the effect of uncertainty in China’s climate policy delivery on global emissions and\ntemperature outcomes. It addresses the following questions: (1) How credible are China’s\nclimate and energy policies? (i.e. how likely is it that the targets they set will be achieved?\n(2) Taking into account the uncertainty stemming from varying credibility, what are the\nrange of emissions outcomes for China over the course of the century? (3) What are the\nimplications of this range for global temperature outcomes under two different scenarios\nfor global ambition?\nThe paper is both innovative and policy relevant. It makes three main contributions. First, it\nadapts a generic framework for assessing policy credibility to the specific institutional and\npolitical context of China, providing a methodological advance. Second, it develops a novel\ndataset that transparently evaluates the credibility of China’s climate and energy targets.\nThird, it quantifies the substantial implications of uncertainty in China’s policy delivery for\nboth emissions trajectories and global temperature outcomes.\nResponse:\nThank you for your careful review of our manuscript and for recognising the innovation and policy\nrelevance of our study. We appreciate the constructive issues raised regarding specific aspects of\nscenario sensitivity and the modelling framework. Specifically, the revisions made in response to\nyour four concerns are outlined below.\nComment 2.1\nA first issue concerns the scope of targets assessed. The paper states that 289 total targets\nwere identified, of which 87 were quantifiable in TIAM, but fewer than 50 were ultimately\nassessed for credibility. It would be helpful to clarify the difference between the 87 and the\n<50 – is the difference due entirely to local/regional targets? More critically, how do the\nexcluded targets factor into the scenarios, if at all? Are they assumed to have no impact,\nand if so, is that assumption defensible? Since the excluded targets are unlikely to be\nrandom (for instance, energy-sector targets are more readily quantifiable in TIAM, whereas\nland-use or efficiency targets may be less so), the criteria for inclusion or exclusion could\nintroduce bias. It would be helpful to clarify the rationale for this filtering step and discuss\nhow it might affect the results.\nResponse:\nThank you for raising this important point. In the revised manuscript, we have enhanced the filtering\nprocess for policy targets and provided a clearer explanation of the steps involved, as well as their\npotential implications (SI Section 1 step 2, lines 131-149).\nIn this updated assessment, we also incorporated China’s newly announced 2035 NDC targets.\nBecause our review begins from policy documents, we recorded all numerical targets contained in\neach document to ensure full coverage. This approach also allowed us to track the policy coverage\nfrequency of each target later. In total, we identified 292 (287+3) targets across 58 policies, which\nincluded some double counting.\nAt the first filtering stage, each target was classified according to whether it was quantifiable. In this\nrevision, we have documented the exclusion reason for each unquantifiable target in the\naccompanying SI_PolicyCredibilityAssessment.xlsx. The main reasons for exclusion are summarised\nas follows:\nSI Table 2: Rationale for filtering TIAM-quantifiable targets\nRationale for filtering\nTIAM-quantifiable Summary of Reasons Examples of Targets\ntargets\n• By 2025, the national oil and gas\nTIAM models technologies in aggregate but cannot pipeline network will expand to\ncapture disaggregated forms, infrastructure length, approximately 210,000 km.\n1. Technology / or building floor area. Similarly, some industrial • By 2025, the energy consumption per\nCommodity Not commodities, such as nonferrous metals, are unit of cement clinker will be reduced by\nRepresented represented in aggregate, preventing explicit 3.7%.\nrepresentation of specific sectors like aluminium and • By 2025, carbon emissions in the\ncement. electrolytic aluminium sector will\ndecrease by 5%.\nWater use, recycling, waste treatment, secondary • By 2025, reduce water use per RMB\n2. Water, Waste, materials, and industrial by-products are outside 10,000 of industrial added value by 16%\nRecycling, and Non- TIAM’s scope. The model does not track paper, compared to 2020.\nEnergy Resource metals, slag, gypsum, manure, straw, red mud, or • By 2025, achieve a nationwide water\nFlows appliance/vehicle recycling, which are actually reuse rate of approximately 94% in large-\nreflected in policies scale industrial sectors.\n• By 2025, total emissions of COD,\nammonia nitrogen, NOx, and VOCs will\ndecrease by 8%, 8%, >10%, and >10%\n3. Pollution, Non- TIAM cannot represent non-GHG pollutants, this\nrespectively compared to 2020.\nGHG Metrics includes PM2.5, COD, NOx, VOCs.\n• By 2025, operational ships will achieve\na 7% reduction in NOx emissions\ncompared to 2020 levels.\n• Since 2021, establish RMB 200 billion\n4. Industrial TIAM does not model regulatory compliance,\nspecial refinancing loan to support the\nStandards & benchmark standards, retrofits, capacity ceilings, or\nclean and efficient use of coal.\nEconomic / Financial process ratios. It also does not track R&D spending,\n• By 2025, increase the digitalization rate\nIndicators or financial/economic targets.\nof production equipment to 55%.\n• By 2025, installed capacity of new types\nof energy storage will reach 30 GW or\nmore.\n• By 2025, renovation area of existing\n5. Non-measurable / Vague target statements lacking clear definitions or\nbuildings will increase by >20 million m²\nAmbiguous Targets involving unmeasurable metrics.\ncompared to 2023.\n• By 2030, all ground vehicles and\nequipment at civil airports will strive to\nbe powered by electricity.\nAfter this filtering, we obtained 92 quantifiable national-level targets (with 2035 NDC and forest\nstock targets included) that could in principle be represented in TIAM. We then manually collected\nadditional information for each target, such as governance level and policy coverage, and removed\ndouble counting at this stage, which yielded the final set of 47 targets included in the credibility\nassessment.\nRegarding the impacts of exclusions, for reasons 1–4, we acknowledge that this TIAM model-based\nstudy may not fully capture the mitigation effects of these excluded targets, and we have highlighted\nthis limitation in both the Discussion and SI section 1\nManuscript line 265-268:\n“However, due to the quantification limits of TIAM-UCL, targets related to water, waste,\nrecycling, air pollution, and climate finance (See SI table 2) are excluded from our\nmodelling practice. As a result, our findings may underestimate the mitigation potential of\nthese measures in GW and RS. Further quantitative research is needed to assess the\ndecarbonisation potential of these targets.”\nFor reason 5, these targets are unlikely to be systematically implemented or tracked in practice.\nTheir exclusion therefore does not alter results but reflect a systemic feature of Chinese climate\npolicymaking: the prevalence of targets that lack measurability or enforceability. This provides\nadditional qualitative insight for this paper. We’ve explicitly discussed this point in the Discussion.\nManuscript line 249-264:\n“The policy gap is particularly evident in the absence of explicit, enforceable sectorial targets for\nfossil fuel phaseout. Vague policy signals such as “strictly limit” coal consumption or “strictly\ncontrol” new coal projects weaken target credibility and allow discretionary interpretation at the\nlocal level3. Meanwhile, the prioritisation of domestic oil and gas production for energy security\nsends mixed signals, potentially reinforcing fossil fuel lock-in and diluting the consistency of\nChina’s decarbonisation agenda (See SI Table 3). Further work is needed to examine how a more\nserious approach to coal phaseout could ensure system reliability, especially by scaling up viable\nalternatives for maintaining power system balance as coal declines.\nOur policy review reveals that the weak enforcement partly stems from ambiguity in central policy\ndesign and deeper structural tensions in China’s climate policymaking and governance. For\ninstance, industrial efficiency regulations define both basic and advanced performance standards\nbut apply only to enterprises above a designated size, narrowing regulatory scope4. Even within\nthe regulated group, the required share of firms meeting advanced standards rarely exceeds 50%,\nreflecting accommodation of regional development disparities (See\nSI_PolicyCredibilityAssessment.xlsx). Many enterprises continue to struggle to meet even the basic\nthresholds. This lack of clarity enables local governments to selectively interpret and implement\npolicies based on local economic priorities. However, due to the quantification limits of TIAM-UCL,\ntargets related to water, waste, recycling, air pollution, and climate finance (See SI table 2) are\nexcluded from our modelling practice. As a result, our findings may underestimate the mitigation\npotential of these measures in GW and RS. Further quantitative research is needed to assess the\ndecarbonisation potential of these targets.”\nWe hope this supplemented clarification could strengthen transparency and reinforce that the\nexclusion of certain targets does not undermine the robustness of our scenario results.\nComment 2.2\nA second issue relates to the credibility assessment methodology. The framework and the\nchoice of indicators (governance level, inclusion in the Five-Year Plan, and progress to date)\nare logical. There is a missed opportunity, however, to engage with literature discussing\nfactors in the target implementation gap (e.g. 1, 2), which could provide a stronger\nfoundation for the indicators chosen. Additionally, while the rationale for weighting\nprogress to date higher than the other two indicators is clear, the weightings in general\nseem somewhat arbitrary. How sensitive are the results to the weightings chosen?\nResponse:\nThank you for this valuable comment. In the revised manuscript, we have engaged with the\nliterature highlighted by the reviewer to provide a stronger basis for our choice of indicators\nManuscript lines 295-303:\n“The definition of ‘policy credibility’ varies in the literature 5,6. Furthermore, there is no standard\nassessment framework for policy credibility across studies7,8. Fransen et al. rely on cross-country\ncomposite indices such as the Climate Policy Score and the Climate Change Performance Index to\ncapture policy adoption and outcome gaps9. Peterson and van Asselt propose five indicators to\nassess country-level NDC implementation risks: NDC ambition, institutional capacity, interest\ngroup opposition, policy inconsistency, and monitoring and enforcement, highlighting common\nchallenges in closing the implementation gap10. While these frameworks are valuable for cross-\ncountry comparison, they are less suited to China’s context, where sectoral targets embedded in\ncentral plans are the primary drivers of emissions outcomes.”\nWe agree that weightings could appear arbitrary, and we therefore test the robustness of our results\nacross five alternative weighting schemes (See SI Section 6.2):\n• 𝜔 :𝜔 :𝜔 = 3:2:5\n𝑔𝑜𝑣 𝐹𝑌𝑃 𝑇𝑁𝑃𝐼\n• 𝜔 :𝜔 :𝜔 = 2:3:5\n𝑔𝑜𝑣 𝐹𝑌𝑃 𝑇𝑁𝑃𝐼\n• 𝜔 :𝜔 :𝜔 = 3:3:4\n𝑔𝑜𝑣 𝐹𝑌𝑃 𝑇𝑁𝑃𝐼\n• 𝜔 :𝜔 :𝜔 = 1:1:1\n𝑔𝑜𝑣 𝐹𝑌𝑃 𝑇𝑁𝑃𝐼\n• 𝜔 :𝜔 :𝜔 = 4:4:2\n𝑔𝑜𝑣 𝐹𝑌𝑃 𝑇𝑁𝑃𝐼\nResults show that our clusters are largely robust across five different weighting combinations. When\nthe weight of the progress indicator is decreased relatively lower to the two governance indicators,\ntwo targets show rating uncertainty:\n• By 2025, renewable energy will account for about 18% of primary energy\nconsumption (previously rated low in main case, now becomes medium).\n• By 2030, electricity will account for 65% of building energy consumption (previously\nrated medium in main case, now becomes low).\nAs a result, we generate a sensitivity case of Calm Sea scenario, donated CS_Low_TNPI,\nwhich\nincludes the first target but excludes the second. Since Green Lights includes all scenario clusters,\nit is unaffected in modelling practice.\nTaken together (comments 2.1 and 2.2), we present China’s domestic sensitivity cases (GHG\nreduction targets for 2035 and the reduced TNPI weight case) in SI section 2.\nSI Table 12. Sensitivity of credibility assessment to weighting schemes\nComment 2.3\nA third issue concerns the treatment of targets after their stated target year. While the\npaper is clear about which targets are assumed to be achieved in which scenarios, it is less\nclear how this achievement is translated into the modelling framework, and in particular\nwhat was assumed about progress on the target indicators following the target year. There\nare two potentially conflicting logics: On the one hand, one could interpret an “achieved”\ntarget as evidence of strong institutional capacity and assume continued progress\nthereafter. Alternatively, an “achieved” target could reflect an unambitious goal, which\nmight correlate with weak implementation capacity, in which case it would not signal a\ntrajectory of sustained improvement. I was not able to decipher how such logic is carried\nforward from the credibility assessment into the scenario narratives and ultimately into the\nIAM results. Greater clarity on this point would be valuable.\nResponse:\nThank you for this insightful comment. In our framework, policy clusters are implemented stepwise\nfrom Great Wall to Green Lights (i.e., achieved, achieved + high, achieved + high + medium, etc.). For\ntargets that are already achieved or even overachieved, we implement the actual realised levels by\n2025 rather than the stated target levels to better reflect China’s existing climate efforts. For\nexample, this applies to indicators such as EV market share or installed solar and wind capacity.\nIn modelling practice, we assume that these achieved levels will not decline after the target year;\ninstead, they are maintained as the minimum baseline from 2025 onwards. As additional targets are\nimplemented from Great Wall to Green Lights, some previously achieved targets are further raised\nto align with higher-level ambition, normative-based goals (e.g., non-fossil share, net-zero\ncommitment). A clear example can be seen in the Calm Sea and Green Lights scenarios: once the\nnet-zero target is introduced, the “achieved” policy levers define the lower bound of the energy\nsystem structure (e.g., sectoral electrification level), while the net-zero and other more system-wide\ntargets further strengthen these levers to ensure that the overall pathway is consistent with\nachieving net zero. This also helps identify the policy gap between the policies currently issued and\nthe commitments made under the net-zero pledge. We have added more clarity on this point in the\nSI line 292-299.\nComment 2.4\nA fourth set of issues relates to the global net zero scenario. The accuracy of assumptions in\nTable 2 should be confirmed. For instance, while emission coverage for Central and South\nAmerica is said to cover CO2 only, the net-zero targets of the largest countries in the region\n(e.g. Brazil, Argentina, Chile, Peru, Colombia) cover all GHGs (3, Likewise for the former\nSoviet Union. The basis for choosing a “representative” country for certain regions should\nalso be clarified – are these countries representative in terms of size of economy, emissions\nprofile, or some other indicator? (For example, on what basis is Nigeria said to be\nrepresentative of Africa?) Alternatively, it is plausible that the results are not particularly\nsensitive to these assumptions, in which case, it would be helpful to clarify that.\nReferences\n1. L. Peterson, H. and van Asselt, Assessing risks to the implementation of NDCs under the\nParis Agreement. Clim. Policy 0, 1–15.\n2. T. Fransen, J. Meckling, A. Stünzi, T. S. Schmidt, F. Egli, N. Schmid, C. Beaton, Taking stock\nof the implementation gap in climate policy. Nat. Clim. Change, 1–4 (2023).\n3. Climate Watch, Net-Zero Tracker (2024). https://www.climatewatchdata.org/net-zero-\ntracker.\n4. Energy and Climate Intelligence Unit, Data Driven Envirolab, New Climate Institute,\nUniversity of Oxford, Net Zero Tracker, (2024); https://zerotracker.net/.\nResponse:\nWe thank the reviewer for highlighting this issue related to our earlier regional net-zero target\nassumptions and for directing us toward country-level emissions and Net Zero Tracker data.\nFor TIAM regions with diverse national pledges (AFR, CSA, FSU, MEA, ODA), we revisited all relevant\ncountries and compiled their 2020 historical GHG emissions and regional shares from Climate Watch\n(https://www.climatewatchdata.org), along with stated net-zero targets and coverage (CO₂-only vs.\nall GHGs) from the Net Zero Tracker (https://zerotracker.net), with data updated to 1 August 2025\n(See Response Table 1). We then filtered for countries with net-zero targets and aggregated their\npledged emissions for 2050, 2060, and 2070 to assess the proportion of each region’s 2020\nemissions committed to net zero (see Manuscript Table 3.b). Instead of imposing uniform regional\nnet-zero constraints, we assume that each region partially achieves net zero based on the share of\ncountries within it that have adopted net-zero targets and converge to net zero GHG emissions by\n2100. The share of emissions contributed by countries within each region is held constant from 2020\nonward. This approach provides a more realistic representation of current ambition levels and likely\nmitigation outcomes in developing regions.\nThe new regional net-zero target construction approach has been updated in manuscript, with full\ndata sources and calculations documented in TIAM_region_net_zero_ target_assumptions.xlsx.\nTable 1. Net-zero pledges by country across TIAM-selected regions\nemissions Share of Year\nTIAM (MtCO₂e, region\nregion Country approx) total End target\nDemocratic\nRepublic of Emissions\nAFR the Congo 707 16.65% reduction target 2030 Not Specified\nCarbon dioxide\nAFR South Africa 525.06 12.36% Net zero 2050 and other GHGs\nCarbon dioxide\nAFR Nigeria 356.46 8.39% Net zero 2060 and other GHGs\nCarbon dioxide\nAFR Egypt 325.48 7.66% Other 2030 and other GHGs\nCarbon dioxide\nAFR Ethiopia 219.58 5.17% Net zero 2050 and other GHGs\nCarbon dioxide\nAFR Tanzania 174.96 4.12% Net zero 2050 and other GHGs\nCarbon dioxide\nAFR Angola 139.08 3.28% Net zero 2050 and other GHGs\nCarbon dioxide\nAFR Sudan 133.48 3.14% Net zero 2050 and other GHGs\nEmissions Carbon dioxide\nAFR Chad 114.31 2.69% reduction target 2030 and other GHGs\nAFR Mozambique 112.05 2.64% Net zero 2050 Not Specified\nEmissions Carbon dioxide\nAFR Zimbabwe 111.37 2.62% reduction target 2030 and other GHGs\nCarbon dioxide\nAFR Zambia 99.06 2.33% Reduction v. BAU 2030 and other GHGs\nCarbon dioxide\nAFR Morocco 95.5 2.25% Reduction v. BAU 2030 and other GHGs\nCarbon dioxide\nAFR Kenya 84.4 1.99% Reduction v. BAU 2035 and other GHGs\nEmissions Carbon dioxide\nAFR Cameroon 76.55 1.80% reduction target 2030 and other GHGs\nEmissions Carbon dioxide\nAFR South Sudan 74.45 1.75% reduction target 2030 and other GHGs\nAFR Libya 68.82 1.62% No target 0 Not Specified\nCarbon dioxide\nAFR Côte d'Ivoire 67.55 1.59% Reduction v. BAU 2030 and other GHGs\nCarbon dioxide\nAFR Uganda 62 1.46% Net zero 2030 and other GHGs\nCarbon dioxide\nAFR Burkina Faso 54.48 1.28% Net zero 2050 and other GHGs\nEmissions Carbon dioxide\nAFR Botswana 53.94 1.27% reduction target 2030 and other GHGs\nCentral African Carbon dioxide\nAFR Republic 53.52 1.26% Net zero 2050 and other GHGs\nCarbon dioxide\nAFR Mali 46.95 1.11% Net zero 2050 and other GHGs\nCarbon dioxide\nAFR Somalia 46.64 1.10% Reduction v. BAU 2035 and other GHGs\nAFR Niger 42.64 1.00% Net zero 2050 Not Specified\nCarbon dioxide\nAFR Madagascar 42.13 0.99% Net zero 2050 and other GHGs\nCarbon dioxide\nAFR Guinea 40.18 0.95% Net zero 2050 and other GHGs\nCarbon Carbon dioxide\nAFR Tunisia 39.2 0.92% neutral(ity) 2050 only\nEmissions Carbon dioxide\nAFR Senegal 32.8 0.77% reduction target 2030 and other GHGs\nEmissions Carbon dioxide\nAFR Congo 30.69 0.72% reduction target 2030 and other GHGs\nCarbon dioxide\nAFR Benin 27.37 0.64% Reduction v. BAU 2030 and other GHGs\nCarbon dioxide\nAFR Namibia 23.46 0.55% Net zero 2050 and other GHGs\nCarbon Carbon dioxide\nAFR Gabon 22.11 0.52% neutral(ity) 2050 and other GHGs\nCarbon dioxide\nAFR Malawi 19.95 0.47% Net zero 2050 and other GHGs\nCarbon Carbon dioxide\nAFR Mauritania 17.99 0.42% neutral(ity) 2030 and other GHGs\nCarbon dioxide\nAFR Ghana 16.93 0.40% Net zero 2060 and other GHGs\nCarbon dioxide\nAFR Liberia 14.95 0.35% Net zero 2050 and other GHGs\nEquatorial Emissions Carbon dioxide\nAFR Guinea 13.11 0.31% reduction target 2050 and other GHGs\nEmissions Carbon dioxide\nAFR Sierra Leone 9.57 0.23% reduction target 2050 and other GHGs\nCarbon dioxide\nAFR Togo 9.34 0.22% Net zero 2050 and other GHGs\nCarbon Carbon dioxide\nAFR Rwanda 9.32 0.22% neutral(ity) 2050 and other GHGs\nCarbon dioxide\nAFR Eritrea 6.88 0.16% Reduction v. BAU 2030 and other GHGs\nEmissions Carbon dioxide\nAFR Burundi 6.57 0.15% reduction target 2050 and other GHGs\nEmissions Carbon dioxide\nAFR Guinea-Bissau 4.56 0.11% reduction target 2030 and other GHGs\nCarbon dioxide\nAFR Gambia 3.92 0.09% Net zero 2050 and other GHGs\nCarbon dioxide\nAFR Lesotho 3.21 0.08% Net zero 2050 and other GHGs\nCarbon dioxide\nAFR Eswatini 2.87 0.07% Reduction v. BAU 2030 and other GHGs\nCarbon dioxide\nAFR Djibouti 1.53 0.04% Net zero 2050 only\nCarbon dioxide\nAFR Seychelles 0.86 0.02% Net zero 2050 and other GHGs\nCarbon dioxide\nAFR Cabo Verde 0.6 0.01% Net zero 2050 and other GHGs\nCarbon dioxide\nAFR Comoros 0.58 0.01% Net zero 2050 and other GHGs\nSão Tomé and Carbon dioxide\nAFR Príncipe 0.4 0.01% Net zero 2050 and other GHGs\nCarbon Carbon dioxide\nCSA Brazil 1537.1 45.64% neutral(ity) 2050 and other GHGs\nCarbon dioxide\nCSA Argentina 394.51 11.71% Net zero 2050 and other GHGs\nCarbon Carbon dioxide\nCSA Colombia 292.4 8.68% neutral(ity) 2050 and other GHGs\nEmissions Carbon dioxide\nCSA Venezuela 240.16 7.13% reduction target 2030 and other GHGs\nCarbon dioxide\nCSA Peru 184.48 5.48% Net zero 2050 and other GHGs\nCSA Bolivia 133.47 3.96% No target 0 Not Specified\nCarbon dioxide\nCSA Paraguay 92.82 2.76% Reduction v. BAU 2030 and other GHGs\nEmissions\nCSA Ecuador 91.42 2.71% reduction target 2035 Not Specified\nCarbon Carbon dioxide\nCSA Chile 50.08 1.49% neutral(ity) 2050 and other GHGs\nEmissions Carbon dioxide\nCSA Guatemala 42.57 1.26% reduction target 2030 and other GHGs\nCarbon dioxide\nCSA Nicaragua 39.49 1.17% Net zero 2050 and other GHGs\nDominican Carbon dioxide\nCSA Republic 39.26 1.17% Net zero 2050 and other GHGs\nCarbon dioxide\nCSA Uruguay 33.29 0.99% Net zero 2050 and other GHGs\nCarbon dioxide\nCSA Cuba 28.55 0.85% Other 2050 and other GHGs\nCarbon dioxide\nCSA Honduras 27.49 0.82% Reduction v. BAU 2030 and other GHGs\nTrinidad and Emissions Carbon dioxide\nCSA Tobago 25.29 0.75% reduction target 2030 and other GHGs\nCarbon dioxide\nCSA Panama 23.62 0.70% Net zero 2050 and other GHGs\nCSA Guyana 18.83 0.56% Net zero 2050 Not Specified\nCarbon dioxide\nCSA Suriname 13.1 0.39% Net zero 2050 and other GHGs\nAbsolute Carbon dioxide\nCSA El Salvador 12.85 0.38% emissions target 2030 and other GHGs\nCSA Haiti 11.61 0.34% Net zero 2050 Not Specified\nCSA Jamaica 10.35 0.31% Net zero 2050 Not Specified\nCarbon dioxide\nCSA Costa Rica 7.42 0.22% Net zero 2050 and other GHGs\nCarbon dioxide\nCSA Belize 6.69 0.20% Net zero 2050 and other GHGs\nCarbon dioxide\nCSA Barbados 3.83 0.11% Net zero 2035 and other GHGs\nAntigua and Carbon dioxide\nCSA Barbuda 3.58 0.11% Net zero 2040 and other GHGs\nEmissions Carbon dioxide\nCSA Bahamas 2.25 0.07% reduction target 2030 and other GHGs\nCarbon dioxide\nCSA Saint Lucia 0.42 0.01% Reduction v. BAU 2030 and other GHGs\nEmissions Carbon dioxide\nCSA Grenada 0.35 0.01% reduction target 2030 and other GHGs\nSaint Vincent\nand the Carbon dioxide\nCSA Grenadines 0.28 0.01% Net zero 2050 and other GHGs\nSaint Kitts and Carbon dioxide\nCSA Nevis 0.26 0.01% Net zero 2050 only\nCarbon Carbon dioxide\nCSA Dominica 0.22 0.01% neutral(ity) 2030 only\nCarbon Carbon dioxide\nFSU Russia 1759.19 59.18% neutral(ity) 2060 and other GHGs\nCarbon Carbon dioxide\nFSU Kazakhstan 318.77 10.72% neutral(ity) 2060 and other GHGs\nCarbon Carbon dioxide\nFSU Ukraine 246.31 8.29% neutral(ity) 2060 and other GHGs\nEmissions Carbon dioxide\nFSU Turkmenistan 204.84 6.89% reduction target 2030 and other GHGs\nCarbon Carbon dioxide\nFSU Uzbekistan 177.87 5.98% neutral(ity) 2050 and other GHGs\nEmissions Carbon dioxide\nFSU Belarus 91.67 3.08% reduction target 2030 and other GHGs\nEmissions Carbon dioxide\nFSU Azerbaijan 50.07 1.68% reduction target 2050 and other GHGs\nCarbon dioxide\nFSU Lithuania 25.02 0.84% Climate neutral 2050 and other GHGs\nEmissions Carbon dioxide\nFSU Tajikistan 17.59 0.59% reduction target 2030 and other GHGs\nCarbon dioxide\nFSU Georgia 17.07 0.57% GHG neutral(ity) 2050 and other GHGs\nCarbon dioxide\nFSU Estonia 15.06 0.51% Net zero 2050 and other GHGs\nCarbon\nFSU Kyrgyzstan 14.06 0.47% neutral(ity) 2050 Not Specified\nCarbon Carbon dioxide\nFSU Moldova 13.38 0.45% neutral(ity) 2050 and other GHGs\nCarbon Carbon dioxide\nFSU Latvia 11.34 0.38% neutral(ity) 2050 and other GHGs\nCarbon dioxide\nFSU Armenia 10.6 0.36% Climate neutral 2050 and other GHGs\nEmissions Carbon dioxide\nMEA Iran 909.92 30.19% reduction target 2030 and other GHGs\nCarbon dioxide\nMEA Saudi Arabia 725.04 24.05% Net zero 2060 and other GHGs\nCarbon dioxide\nMEA Turkey 474.98 15.76% Net zero 2053 and other GHGs\nUnited Arab Carbon dioxide\nMEA Emirates 242.06 8.03% Net zero 2050 and other GHGs\nEmissions Carbon dioxide\nMEA Qatar 121.82 4.04% reduction target 2030 and other GHGs\nCarbon\nMEA Kuwait 121.39 4.03% neutral(ity) 2060 Not Specified\nCarbon dioxide\nMEA Oman 95.24 3.16% Net zero 2050 and other GHGs\nMEA Israel 81.81 2.71% Net zero 2050 Not Specified\nMEA Syria 77.14 2.56% No target 0 Not Specified\nMEA Bahrain 57.33 1.90% Net zero 2060 Not Specified\nCarbon dioxide\nMEA Yemen 30.08 1.00% Net zero 2030 and other GHGs\nCarbon dioxide\nMEA Jordan 29.68 0.98% Reduction v. BAU 2030 and other GHGs\nMEA Lebanon 26.99 0.90% Net zero 2050 Not Specified\nCarbon dioxide\nMEA Brunei 12.63 0.42% Net zero 2050 and other GHGs\nCarbon dioxide\nMEA Cyprus 8.05 0.27% Climate neutral 2050 and other GHGs\nCarbon dioxide\nODA Indonesia 1449.43 31.57% Net zero 2060 and other GHGs\nCarbon dioxide\nODA Pakistan 578.89 12.61% Net zero 2050 and other GHGs\nCarbon dioxide\nODA Vietnam 473.76 10.32% Net zero 2050 and other GHGs\nCarbon dioxide\nODA Thailand 414.29 9.02% Net zero 2065 and other GHGs\nCarbon dioxide\nODA Malaysia 371.93 8.10% Net zero 2050 and other GHGs\nCarbon dioxide\nODA Bangladesh 242.74 5.29% Net zero 2030 and other GHGs\nCarbon dioxide\nODA Philippines 235.96 5.14% Reduction v. BAU 2030 and other GHGs\nCarbon dioxide\nODA Myanmar 231.83 5.05% Net zero 2050 only\nCarbon dioxide\nODA North Korea 88.66 1.93% Reduction v. BAU 2030 and other GHGs\nCarbon dioxide\nODA Cambodia 80.35 1.75% Net zero 2050 and other GHGs\nEmissions Carbon dioxide\nODA Mongolia 76.63 1.67% reduction target 2030 and other GHGs\nCarbon dioxide\nODA Singapore 59.8 1.30% Net zero 2050 and other GHGs\nCarbon dioxide\nODA Nepal 58.45 1.27% Net zero 2045 only\nPapua New Carbon Carbon dioxide\nODA Guinea 50.15 1.09% neutral(ity) 2050 only\nSolomon Carbon dioxide\nODA Islands 45.82 1.00% Net zero 2050 and other GHGs\nCarbon dioxide\nODA Laos 45.35 0.99% Net zero 2050 and other GHGs\nCarbon Carbon dioxide\nODA Sri Lanka 39.53 0.86% neutral(ity) 2050 and other GHGs\nCarbon dioxide\nODA Afghanistan 32.08 0.70% Reduction v. BAU 2030 and other GHGs\nCarbon\nODA Mauritius 6.43 0.14% neutral(ity) 2070 Not Specified\nCarbon dioxide\nODA East Timor 5.57 0.12% Net zero 2050 and other GHGs\nAbsolute Carbon dioxide\nODA Maldives 2.13 0.05% emissions target 2035 and other GHGs\nCarbon dioxide\nODA Bhutan 0.7 0.02% Carbon negative 2050 and other GHGs\nCarbon dioxide\nODA Vanuatu 0.53 0.01% Net zero 2050 and other GHGs\nCarbon dioxide\nODA Samoa 0.5 0.01% Net zero 2050 and other GHGs\nCarbon dioxide\nODA Tonga 0.3 0.01% Net zero 2050 and other GHGs\nCarbon dioxide\nODA Kiribati 0.09 0.00% Net zero 2050 only\nCarbon dioxide\nODA Fiji -0.86 -0.02% Net zero 2050 and other GHGs\nManuscript Table 3. (b) Net-zero pledge coverage rate and assumptions for TIAM selected region\n2050 2060 2070 2100\nAfrica 42.71% 52.10% 52.1% 100%\n72.29% 100%\nCentral and South America 72.29% 72.29%\nFormer Soviet Union 8.37% 86.55% 86.55% 100%\nMiddle East 31.98% 61.96% 61.96% 100%\nOther Developing Asia 49.78% 81.35% 90.5% 100%\nReference\n1. He, Y., Piao, S., Ciais, P., Xu, H. & Gasser, T. Future land carbon removals in China\nconsistent with national inventory. Nat. Commun. 15, 10426 (2024).\n2. Pye, S. et al. The TIAM-UCL Model (Version 4.1.1) Documentation. (2020).\n3. CCP & State Council. China’s Achievements, New Goals and New Measures for Nationally\nDetermined Contributions. (2021).\n4. NDRC. Action Plan for Strict Energy Efficiency Constraints to Promote Energy\nConservation and Carbon Reduction in Key Industries (2021-2025).\nhttps://www.ndrc.gov.cn/xxgk/zcfb/tz/202110/t20211021_1300583_ext.html (2021).\n5. Brunner, S., Flachsland, C. & Marschinski, R. Credible commitment in carbon policy. Clim.\nPolicy 12, 255–271 (2012).\n6. Jacobs, A. M. Policy Making for the Long Term in Advanced Democracies. Annu. Rev. Polit.\nSci. 19, 433–454 (2016).\n7. Victor, D. G., Lumkowsky, M. & Dannenberg, A. Determining the credibility of\ncommitments in international climate policy. Nat. Clim. Change 12, 793–800 (2022).\n8. Teng, F. Ambitious and credible pledges. Nat. Clim. Change 12, 779–780 (2022).\n9. Fransen, T. et al. Taking stock of the implementation gap in climate policy. Nat. Clim.\nChange 13, 752–755 (2023).\n10. Peterson, L. & van Asselt, H. Assessing risks to the implementation of NDCs under\nthe Paris Agreement. Clim. Policy 0, 1–15."}, {"round": 2, "reviewer_comments": {"reviewer_1": "In this revised version, the authors have substantially strengthened the manuscript and have addressed most of the\nconcerns raised in the previous review. The paper now presents a timely and policy-relevant assessment of China’s climate\npolicy credibility and its global implications, combining a structured policy review with scenario-based integrated\nassessment modelling. I have only a few remaining minor points—mainly clarifications for transparency and small\nfigure/presentation refinements—that would further improve readability and reproducibility before publication.\n1. The manuscript/abstract could be read as implying that the structured credibility assessment was applied to all identified\ntargets (e.g., 292 targets across policy documents). However, the Supplementary Information indicates a multi-step filtering\nprocess: 292 numerical targets were identified, then a subset of “quantifiable” targets was retained (reported as 90, including\nduplicates), which was further consolidated to a final set of 47 targets that were actually evaluated/scored for credibility.\nPlease clarify this explicitly in the main text/abstract to avoid confusion—e.g., state that the study identifies 292 targets but\nthe credibility scoring is conducted on the filtered set of quantifiable targets (final N=47), and briefly explain the filtering\nrationale\n2. While the manuscript states that scenario narratives are informed by the policy credibility assessment, the mapping\nbetween individual policy credibility scores and the quantified scenario assumptions remains insufficiently specified. This\nmay lead to confusion for non-specialist readers regarding the purpose of the credibility assessment, how it influences\nscenario construction, and how it ultimately affects the modeling results. Given that relevant tables and detailed mappings\nalready appear to be provided in the Supplementary Information, the authors are encouraged to add a brief explanatory\nbridge in the Introduction and/or Methods sections.\n3. The authors are encouraged to carefully check the consistency and completeness of cross-referencing between the main\ntext and the Supplementary Information (SI). At present, several tables and figures in the SI appear not to be explicitly cited\nin either the main text or within the SI itself. For example, in the SI (around line 140), the text refers to “(see SI Table X, Step\n3)”, the corresponding table reference is unclear. In addition, SI Tables 4, 5, 7, 8, 11, and 12 do not appear to be cited\nanywhere in the main manuscript or the SI text. A similar issue applies to several supplementary figures.\n4. Figure 1(b): The dashed line indicating the share of renewable energy (black) overlaps with the coal bar for the year 2020,\nwhich is also shown in black. This makes it difficult to identify the renewable share value for that year.\n5. Figures 2(a) and 2(b): In the GW scenario, the grey uncertainty band overlaps with the red shaded area representing the\nRS scenario, resulting in a bluish color that is visually ambiguous. Adjusting the plotting order (layering) or modifying\ntransparency/color choices would help avoid color blending and improve interpretability.\n6. Figure 3(a): Some text elements are obscured or truncated. On the left-hand side, labels are partially covered by lines,\nwhile on the right-hand side the word “recommendation” appears broken or cut off. Please revise the layout to ensure all text is\nfully visible and legible.\n7. Figure 3(b): (1) The governance level indicator (from the CCP to ministries) is currently encoded solely through bubble\nsize. However, the visual contrast between sizes appears insufficient to clearly distinguish different administrative levels,\nparticularly between the largest bubbles (CCP & State Council) and the medium-sized bubbles (e.g. NDRC). The authors\nmay consider combining symbol shape with size to enhance visual differentiation—for example, using large circles for CCP-\nlevel targets and smaller symbols (e.g. triangles or stars) for targets issued by the NDRC and ministries.\n(2) The figure contains a large number of data points and employs multiple discontinuous or non-linear axis segments,\nwhich results in substantial crowding in key regions (e.g., credibility scores between ~0.7 and 1.1). This leads to overlapping\nlabels and makes it difficult to identify individual policy targets. The authors may wish to explore alternative\nvisualization strategies, such as segmented or logarithmic scaling, selective zoom-in in critical ranges, or inset panels focusing on dense\nregions, to improve legibility.\n(3) In light of the scenario narratives developed in this section, the figure could be further strengthened by explicitly\nhighlighting or grouping key clusters of targets. For example, distinguishing targets with high credibility scores but lagging\nprogress (which may require strong policy reinforcement as represented in the GL scenario), versus targets with low\ncredibility yet high mitigation relevance (which may be deprioritized under the GW scenario), would enhance the narrative\ncoherence between the credibility assessment and the subsequent scenario analysis.\n8. Line 356: The text states “Where B_v,T_v,T_v are the base, target, and current years”, but this should likely write\nB_y,T_y,T_y.\n9. Line 365-366: While the manuscript states that the credibility score combines three components and tests five alternative\nweighting schemes (SI Section 1), the main text does not specify the actual weights applied to each indicator, nor clarify\nwhich weighting scheme(s) underpin the results shown in Fig. 3 and the subsequent scenario analysis. Given that the\ncredibility score plays a central role in informing scenario narratives, a brief description of the weighting logic and reference\nto the relevant SI tables in the main text would improve transparency and reproducibility.\n10. The analysis focuses on 47 quantifiable targets due to model constraints and data availability. While this filtering is\nunderstandable, it may introduce a systematic selection bias by excluding non-quantifiable but policy-relevant measures\n(e.g., water, waste, recycling, air pollution control, and detailed industrial or building efficiency policies). As a result,\nmitigation contributions from easily measurable sectors such as power and transport may be overstated, which may shift\nsectoral emphasis and alter perceived feasibility/costs; please discuss likely direction and (if possible) sensitivity. The\nauthors are encouraged to discuss how this sectoral imbalance may affect the estimated feasibility, costs, and structural\ncharacteristics of China’s net-zero transition, and to clarify the likely direction of the bias introduced.\n11. The first Results subsection (“Chinese Decarbonization and Energy Transformation”) presents outcomes of China’s\nenergy system transformation under different domestic policy scenarios (GW–GL) without explicitly stating the assumed\nglobal context. Please specify which global context is used for Fig. 1, and briefly note whether the qualitative conclusions\nare robust across both contexts. Nevertheless, a brief clarification in the text—stating that these results are “global context–\nneutral” or not materially affected by ROW assumptions—would help avoid potential confusion for readers who might\notherwise expect Figure 1 to correspond to a specific global scenario.", "reviewer_2": "I reviewed an earlier version of this manuscript and found it to be a strong contribution, pending revisions. I have now\nreviewed the revised version and the authors’ responses to reviewers. I am satisfied that the authors have adequately\naddressed the concerns raised in the initial round of review. Their revisions are appropriate and improve the clarity and\nrobustness of the manuscript. I recommend the manuscript for publication."}, "author_response": "Responses to Reviewers\nNCOMMS-25-41170A\nDecember 2025\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nIn this revised version, the authors have substantially strengthened the manuscript and have\naddressed most of the concerns raised in the previous review. The paper now presents a\ntimely and policy-relevant assessment of China’s climate policy credibility and its global\nimplications, combining a structured policy review with scenario-based integrated\nassessment modelling. I have only a few remaining minor points—mainly clarifications for\ntransparency and small figure/presentation refinements—that would further improve\nreadability and reproducibility before publication.\nResponse:\nThank you for you positive and constructive assessment of the revised manuscript. We are\npleased that the revisions have addressed most of the concerns raised and strengthened the\npolicy relevance and clarity of the paper. We have carefully addressed the remaining points\nto further improve transparency, readability, and reproducibility. The corresponding\nrevision and responses are presented below.\n1. The manuscript/abstract could be read as implying that the structured credibility\nassessment was applied to all identified targets (e.g., 292 targets across policy documents).\nHowever, the Supplementary Information indicates a multi-step filtering process: 292\nnumerical targets were identified, then a subset of “quantifiable” targets was retained\n(reported as 90, including duplicates), which was further consolidated to a final set of 47\ntargets that were actually evaluated/scored for credibility. Please clarify this explicitly in the\nmain text/abstract to avoid confusion—e.g., state that the study identifies 292 targets but\nthe credibility scoring is conducted on the filtered set of quantifiable targets (final N=47),\nand briefly explain the filtering rationale.\nResponse:\nThank you for this suggestion. We have clarified the policy filtering and quantification\nprocess in both the Introduction and Methods to avoid any misunderstanding.\nIn the Introduction (Lines 40-42):\n“A total of 292 numerical targets across 58 policy documents are reviewed. Of these, 47\nquantifiable targets are filtered and implemented in the energy system model (see\nMethods).”\nIn the Methods (Lines 314-321):\n“In total, 58 policy documents were analysed, covering 292 numerical targets, including\nChina’s updated 2035 NDC.\nGiven the quantification limits of the TIAM-UCL model and the fact that some targets are\nunmeasurable (e.g. all ground vehicles and equipment at civil airports will strive to be\npowered by electricity by 2030), 47 targets were filtered and included in our credibility\nassessment (See Figure 3. (b)). The detailed reasons and implications for target non-\nquantifiability are discussed in SI Section 1. Target-level details are provided in the\nSupplementary Data. In the context of China, we developed and applied a credibility rating\nbased on three key policy characteristics:”\n2. While the manuscript states that scenario narratives are informed by the policy credibility\nassessment, the mapping between individual policy credibility scores and the quantified\nscenario assumptions remains insufficiently specified. This may lead to confusion for non-\nspecialist readers regarding the purpose of the credibility assessment, how it influences\nscenario construction, and how it ultimately affects the modeling results. Given that\nrelevant tables and detailed mappings already appear to be provided in the Supplementary\nInformation, the authors are encouraged to add a brief explanatory bridge in the\nIntroduction and/or Methods sections.\nResponse:\nThank you for your suggestions. To better clarify the policy quantification process and the\nkey underlying assumptions, and in line with the manuscript’s conciseness requirements, we\nadded an additional subsection in the Methods section entitled “Policy quantification across\nChinese energy transition scenarios” and cross-refer to the relevant details in the SI.\nLines 476-488:\n“Policy quantification is guided by the assessed credibility of individual targets—classified as\nAchieved, High, Medium, or Low; and by scenario narratives developed using a\nmorphological approach. The Great Wall scenario, which enforces only Achieved targets,\ntherefore represents the most conservative pathway, reflecting minimal policy delivery.\nFor targets that have already been achieved or overachieved, we implement the realised levels\nobserved by 2025 rather than the stated target values, in order to more accurately capture\nChina’s existing climate efforts. Beyond the target year, these achieved levels are assumed to\nbe maintained and form a minimum baseline from 2025 onwards.\nAcross scenarios, policy implementation expands progressively from higher- to lower-\ncredibility targets. Moving from Great Wall to Green Lights, the assumed strength of policy\ndelivery increases, with an expanding set of targets implemented, ultimately culminating in\nthe full realisation of China’s overarching climate commitment: carbon neutrality by 2060 or\nearlier (assumed as 2050). Detailed mappings between specific targets and scenarios are\nprovided in SI Table 6..”\n3. The authors are encouraged to carefully check the consistency and completeness of\ncross-referencing between the main text and the Supplementary Information (SI). At\npresent, several tables and figures in the SI appear not to be explicitly cited in either the\nmain text or within the SI itself. For example, in the SI (around line 140), the text refers to\n“(see SI Table X, Step 3)”, the corresponding table reference is unclear. In addition, SI Tables\n4, 5, 7, 8, 11, and 12 do not appear to be cited anywhere in the main manuscript or the SI\ntext. A similar issue applies to several supplementary figures.\nResponse:\nThank you for your careful review, and we apologise for this oversight. We have now\nthoroughly checked the consistency and completeness of cross-referencing between the\nmain text and the Supplementary Information. All SI tables and figures are now explicitly\ncited in the main manuscript, and unclear references (e.g., “see SI Table X, Step 3”) have\nbeen corrected.\nIn particular, SI Tables 4, 5, 7, 8, and 11–12 are now cross-referenced in the Figure 4 caption,\nwhich has been revised to read from lines 467-471:\n“To quantify the scenario narratives in TIAM-UCL, we examine five key metrics and their\nassociated uncertainties in SI Section 4, along with the corresponding modelling\nassumptions: national climate policy delivery (SI Section 4.1, Tables 5 and 6), GDP growth ( SI\nSection 4.2, Figure 3), population(SI Section 4.3, Tables 7 and 8), clean technology cost\nreductions(SI Section 4.4, Table 9), and CCS deployment rates(SI Section 4.5, Table 10).”\n4. Figure 1(b): The dashed line indicating the share of renewable energy (black) overlaps\nwith the coal bar for the year 2020, which is also shown in black. This makes it difficult to\nidentify the renewable share value for that year.\nResponse:\nThank you for pointing this out. We changed the colour of the dashed line in Figure 1(b) to\navoid overlap with the coal bar in 2020. The revised figure has been updated in the\nmanuscript.\n5. Figures 2(a) and 2(b): In the GW scenario, the grey uncertainty band overlaps with the red\nshaded area representing the RS scenario, resulting in a bluish color that is visually\nambiguous. Adjusting the plotting order (layering) or modifying transparency/color choices\nwould help avoid color blending and improve interpretability.\nResponse:\nThank you for raising this point. We have revised Figures 2(a) and 2(b) by adjusting the\ntransparency and plotting order, and by increasing the colour contrast to avoid colour\nblending between the GW uncertainty band and the RS shaded area. The revised figures are\nnow clearer and easier to interpret.\n6. Figure 3(a): Some text elements are obscured or truncated. On the left-hand side, labels\nare partially covered by lines, while on the right-hand side the word “recommendation”\nappears broken or cut off. Please revise the layout to ensure all text is fully visible and\nlegible.\nResponse:\nThank you for your suggestion. We’ve adjusted the text and layout as much as possible to\navoid text broken or cut off issue. New revised figure 3(a) now is fully visible.\n7. Figure 3(b): (1) The governance level indicator (from the CCP to ministries) is currently\nencoded solely through bubble size. However, the visual contrast between sizes appears\ninsufficient to clearly distinguish different administrative levels, particularly between the\nlargest bubbles (CCP & State Council) and the medium-sized bubbles (e.g. NDRC). The\nauthors may consider combining symbol shape with size to enhance visual differentiation—\nfor example, using large circles for CCP-level targets and smaller symbols (e.g. triangles or\nstars) for targets issued by the NDRC and ministries.\n(2) The figure contains a large number of data points and employs multiple discontinuous or\nnon-linear axis segments, which results in substantial crowding in key regions (e.g.,\ncredibility scores between ~0.7 and 1.1). This leads to overlapping labels and makes it\ndifficult to identify individual policy targets. The authors may wish to explore alternative\nvisualization strategies, such as segmented or logarithmic scaling, selective zoom-in in\ncritical ranges, or inset panels focusing on dense regions, to improve legibility.\n(3) In light of the scenario narratives developed in this section, the figure could be further\nstrengthened by explicitly highlighting or grouping key clusters of targets. For example,\ndistinguishing targets with high credibility scores but lagging progress (which may require\nstrong policy reinforcement as represented in the GL scenario), versus targets with low\ncredibility yet high mitigation relevance (which may be deprioritized under the GW\nscenario), would enhance the narrative coherence between the credibility assessment and\nthe subsequent scenario analysis.\nResponse:\nThank you for your constructive suggestions.\n(1)–(2), we tested all of the approaches you proposed. However, some options increased\nvisual complexity and reduced overall interpretability (e.g., logarithmic scaling and zoom-\nins/inset views for dense ranges). We therefore implemented a combination of symbol\nshape and bubble size to better differentiate governance levels. In addition, we refined the\naxis break from 0.3-1.2, increased the relative space allocated to the crowded mid-range,\nand slightly enlarged the overall figure to improve legibility.\n(3) To strengthen the link between the credibility assessment and the scenario narratives,\nwe added credibility thresholds to group targets into clusters and highlighted the achieved\ntargets in grey. We also expanded the figure caption to clarify how the figure should be\ninterpreted and how the identified clusters relate to the scenario narratives and\nquantification. The revised figure and caption have been incorporated into the manuscript.\nFigure 3.b Credibility assessment of China’s climate and energy goals across sectors (details of each target\nassessment are provided in SI Section 2). Each shaped bubble represents a policy target. Bubble colour\nindicates the sector affected, and bubble shape reflects the level of government issuing the target (from the\nCCP and State Council to ministries, i.e., from higher to lower authority). The x-axis shows the raw progress\ntowards each target (as of October 2025), and the y-axis reports the final credibility score, calculated as a\nweighted average of the three evaluation criteria described in the text (𝜔 :𝜔 :𝜔 =3:2:5).\n𝑔𝑜𝑣 𝐹𝑌𝑃 𝑇𝑁𝑃𝐼\nThe grey\nregion indicates targets that had been achieved or overachieved by the review date and are therefore treated\nas implemented from the GW scenario onwards. The dashed lines mark the credibility thresholds: high\ncredibility (score ≥ the 67th percentile of all unachieved targets) and medium credibility (33rd–67th\npercentile).\n8. Line 356: The text states “Where B_v,T_v,T_v are the base, target, and current years”, but\nthis should likely write B_y,T_y,T_y.\nResponse:\nThank you for pointing this out, and we apologise for the oversight. We have corrected this\nnotation and have also checked and revised the corresponding text in the Supplementary\nInformation where relevant.\n9. Line 365-366: While the manuscript states that the credibility score combines three\ncomponents and tests five alternative weighting schemes (SI Section 1), the main text does\nnot specify the actual weights applied to each indicator, nor clarify which weighting\nscheme(s) underpin the results shown in Fig. 3 and the subsequent scenario analysis. Given\nthat the credibility score plays a central role in informing scenario narratives, a brief\ndescription of the weighting logic and reference to the relevant SI tables in the main text\nwould improve transparency and reproducibility.\nResponse:\nThank you for this suggestion. We have clarified the weighting scheme used to construct the\ncredibility score in the Figure 3 caption (as shown above) and both the main text (Lines 371-\n381)\n“For targets still in progress (raw progress < 1), we classify them using a weighted credibility\nscore. Each target’s score combines three components: governance level, inclusion in the\n14th Five-Year Plan, and the TNPI. Figure 3. (b) presents the overall credibility score using\nweights where ω_gov:ω_FYP:ω_TNPI=3:2:5. Targets are then clustered into categories as\nfollows: High credible (score ≥ 67th percentile), Medium credible (33rd–67th percentile), and\nLow credible (below 33rd percentile). To evaluate the sensitivity of credibility scores to\nweighting scheme and ensure the robustness of the clustering, we tested five alternative\nweighting schemes (see SI Section 1). Across weighting schemes, target classifications are\nlargely robust. Only two targets shift category when the TNPI weight is lower than the other\ntwo indicators (see SI Table 13). We therefore adopt the consistent clustering results from\nthe first four weighting schemes for our subsequent scenario quantification and report an\nadditional weighting-related sensitivity case in SI Section 6.1. SI Figure 5 and 6 indicate that\nweighting-related sensitivity does not undermine our findings on China’s energy transition\nand its emission implications.”\n10. The analysis focuses on 47 quantifiable targets due to model constraints and data\navailability. While this filtering is understandable, it may introduce a systematic selection\nbias by excluding non-quantifiable but policy-relevant measures (e.g., water, waste,\nrecycling, air pollution control, and detailed industrial or building efficiency policies). As a\nresult, mitigation contributions from easily measurable sectors such as power and transport\nmay be overstated, which may shift sectoral emphasis and alter perceived feasibility/costs;\nplease discuss likely direction and (if possible) sensitivity. The authors are encouraged to\ndiscuss how this sectoral imbalance may affect the estimated feasibility, costs, and\nstructural characteristics of China’s net-zero transition, and to clarify the likely direction of\nthe bias introduced.\nResponse:\nThank you for raising this concern. We acknowledge that the mitigation potential of some\npolicy-relevant but non-quantifiable targets is not fully captured in our modelling\nframework. However, we do not expect our focus on 47 quantifiable targets to materially\noverstate mitigation contributions from easily measurable energy-related sectors (e.g.,\npower and transport) in a way that would change the main conclusions. IEA 2023 data\nindicate that around 96% of China’s CO₂ emissions originate from energy-related sectors,\nincluding electricity and heat production, transport, industry, and buildings (residential,\ncommercial, and public services), which are largely covered by our policy assessment and\nthe TIAM-UCL modelling scope. Therefore, the implications of excluding targets related to\nwater, waste, recycling, air pollution control, and climate finance are unlikely to materially\naffect our findings on China’s overall energy transition and future CO₂ emissions, although a\nmore comprehensive assessment of these non-energy measures remains an important\ndirection for future work.\nTo improve transparency, we added clarifications on the potential selection bias and its\nlikely direction in both Discussion and SI.\nIn Discussion lines 268-275:\n“However, due to the quantification limits of TIAM-UCL, targets related to water, waste,\nrecycling, air pollution, and climate finance are excluded from our modelling (see SI Table 2).\nNonetheless, the 47 modelled targets cover all major energy-related sectors, including\nelectricity and heat production, transport, industry, and buildings. Together, these sectors\naccount for around 96% of China’s CO₂ emissions, based on IEA 2023 data20. Therefore, any\nunderestimation arising from the excluded targets is unlikely to materially affect our\nconclusions on China’s energy transition and future carbon emissions, although assessing\nthese effects more comprehensively is an important direction for future research.”\n11. The first Results subsection (“Chinese Decarbonization and Energy Transformation”)\npresents outcomes of China’s energy system transformation under different domestic policy\nscenarios (GW–GL) without explicitly stating the assumed global context. Please specify\nwhich global context is used for Fig. 1 and briefly note whether the qualitative conclusions\nare robust across both contexts. Nevertheless, a brief clarification in the text—stating that\nthese results are “global context–neutral” or not materially affected by ROW assumptions—\nwould help avoid potential confusion for readers who might otherwise expect Figure 1 to\ncorrespond to a specific global scenario.\nResponse:\nThank you for pointing this out. We agree that clarifying the global context for Figure 1 is\nimportant. To maintain a coherent flow of the results subsection, we added this clarification\nin the Figure 1 caption (Lines 180-183):\n“It should be noted that China related modelling results presented in Figure 1 are based on\nthe global NDC context. However, due to the underlying TIMES model structure, China’s\ndomestic energy transition is primarily driven by end-use demand and domestic policy\nconstraints and is not materially affected by assumptions about the rest of the world.”\nReviewer #2 (Remarks to the Author):\nI reviewed an earlier version of this manuscript and found it to be a strong contribution,\npending revisions. I have now reviewed the revised version and the authors’ responses to\nreviewers. I am satisfied that the authors have adequately addressed the concerns raised in\nthe initial round of review. Their revisions are appropriate and improve the clarity and\nrobustness of the manuscript. I recommend the manuscript for publication.\nResponse:\nThank you very much for your positive assessment of the revised manuscript. We are glad\nthat the revisions have adequately addressed the reviewer’s concerns and improved the\noverall clarity and robustness of the work. We appreciate the reviewer’s recommendation\nfor publication."}]

Decision letter