Abstract
Introduction:
This study investigates how U.S. tariffs on Chinese photovoltaic (PV) products affect the embodied carbon emissions and decarbonization trajectory of China’s PV power sector.
Methods:
We couple a multi-regional input-output model, the Tapio decoupling model, and the GTAP-E computable general equilibrium model. Historical data from 2000–2022 establish baseline trends, while three policy scenarios are simulated for 2023–2030, with a parameter sweep identifying critical tariff thresholds.
Results:
Historical analysis reveals progressive decoupling (1% output growth drives only 0.5% embodied carbon increase). Under extreme tariffs (TTB), China’s PV embodied carbon rebounds by 4.9% cumulatively during 2025–2027, while U.S. emissions surge by 30.5%, reflecting a lose–lose outcome. A critical tariff threshold of 18–19% is identified, beyond which China’s PV emissions reverse from decline to growth. Although China regains strong decoupling by 2030, extreme tariffs still reduce cumulative emission reductions by 41%.
Discussion:
These findings reveal a nonlinear relationship between tariff intensity and decarbonization outcomes, highlighting how geopolitical trade measures undermine climate goals and underscoring the need for climate-resilient international trade rules.
1 Introduction
Photovoltaic (PV) power generation plays a pivotal role in advancing the global low-carbon energy transition (). As the world’s largest PV manufacturer and market, China’s technological and cost innovations have significantly contributed to this transition (). However, recent trade barriers led by U.S. tariffs on Chinese PV products are disrupting global trade patterns (), potentially undermining clean energy diffusion by altering technology flows and supply chains (; ). Two underexplored questions thus emerge: How do U.S. PV tariffs affect China’s PV sector decarbonization path? And how can industrial policy align with climate goals amid trade tensions?
Existing research has widely examined PV trade policy impacts. Early studies examined the political economy of the U.S.-China solar dispute, highlighting how trade sanctions were imposed despite opposition from a majority of U.S. solar firms (). Recent work highlights non-tariff barriers; for instance, green trade measures may even boost exports (). Environmental analyses focus largely on product-level carbon accounting, revealing higher energy efficiency in Chinese PV manufacturing () and the role of economies of scale in cutting emissions and costs (). Other studies warn that tariffs could substantially reduce global PV deployment and associated emission savings ().
While existing studies have rigorously examined how solar tariffs affect downstream deployment and associated emission savings (), as well as the welfare and distributional impacts of trade disputes (; ), a distinct question remains underexplored: how do such tariffs affect the upstream decarbonization trajectory of the PV manufacturing sector itself? Prior work has established that tariffs make solar more expensive, limiting deployment and therefore emission reductions–a relatively intuitive finding. This study goes a step further by evaluating not only the additional emissions created by slowing PV uptake but also the emissions trajectory of solar power generation as a sector, through the dynamics of upstream manufacturing and supply chain restructuring. This distinction matters because to decarbonize the power sector, we need not only to deploy PV capacity but also to eliminate the embodied carbon emissions generated in producing PV equipment. Trade barriers that disrupt global supply chains may inadvertently reverse progress on the latter, creating an “tariff-induced carbon rebound” that prior trade-focused analyses have not captured. Specifically, we quantify a critical tariff threshold (18%–19%) beyond which China’s PV sector switches from emissions decline to absolute growth–a nonlinear regime shift that extends the existing literature’s linear treatment of tariff-environment relationships. To address these gaps, this study employs a coupled multi-model framework—integrating a multi-regional input-output model, decoupling analysis, and a dynamic general equilibrium model—to simulate China’s PV power sector response under varying tariff shocks. We quantitatively identify policy thresholds that may erode environmental benefits, offering a systematic assessment of the trade-climate interplay and a scientific basis for aligning clean energy development with global climate governance.
2 Materials and methods
This study constructs an integrated analytical framework coupling a multi-regional input-output (MRIO) model, the Tapio decoupling model, and the GTAP-E computable general equilibrium model (Figure 1). By leveraging multi-source data, we perform cross-period prediction and calibration of key variables in the GTAP-E database. Under various trade barrier scenarios, the output change rates and projected values derived from the GTAP-E model are combined with emission coefficients calculated from EXIOBASE MRIO analysis to jointly estimate future sectoral carbon emissions and embodied carbon emissions. These values are then incorporated into the Tapio decoupling model to assess future decoupling states.
FIGURE 1
2.1 Methods
This study systematically analyzes the impact of U.S. PV tariffs on the decarbonization process of China’s PV power sector. First, based on a MRIO model, we conduct a historical evolution analysis from three dimensions: the dynamic output scale of the PV power sector, the life-cycle embodied carbon emission trajectory, and the evolution patterns of carbon emission intensity.
Building upon this foundation, we introduce the Tapio decoupling model to quantitatively measure the decoupling states between sectoral output and carbon emissions during 2000–2022, thereby revealing its decarbonization evolution characteristics.
The study further employs the GTAP-E computable general equilibrium model to establish three policy scenarios - Business-as-Usual (BAU), High Tariff Barrier (HTB), and Trump Tariff Benchmark (TTB) - simulating and projecting the output elasticity and price transmission effects of China’s PV power sector during 2023–2030.
2.1.1 MRIO model and calculation of carbon-embodied emissions
The MRIO model can be represented by Equation 1 as follows:where denotes the output column vector, denotes the direct consumption coefficient matrix, and denotes the final demand column vector. From this, we can obtain Equation 2:where is the inverse matrix.
In this study, “embodied carbon emissions” for the PV power generation sector refer to the total direct and indirect CO2 emissions induced along the entire upstream supply chain to produce PV electricity, including the direct operational emissions during PV plant operation (which are negligible for solar PV, but not for other electricity sources such as fossil fuel generation) (). To calculate the embodied carbon emissions from PV power generation in China, this study refers to and introduces a carbon emission factor matrix (Equation 3).where denotes sectoral production carbon emissions, denotes total output, and denotes carbon emissions per unit of output, calculated from environmental accounts and input-output tables in the EXIOBASE database.where denotes embodied carbon emissions (Equation 4).
2.1.2 Tapio decoupling model
In this study, the Tapio decoupling model was used to calculate the decoupling index between economic growth and embodied carbon emissions in China’s PV power sector (Equation 5).where denotes the embodied carbon decoupling index in year for the electricity generation sector. The superscript represents the target year; the superscript represents the base year. denotes the rate of change in embodied carbon emissions, and represents the rate of change in sectoral output size. This study uses 2000 as the base year. We chose 2000 as the base year for the decoupling analysis because it is the earliest year in the EXIOBASE database that reports the PV power generation sector as a separate category. Earlier years aggregate PV with other electricity sources, preventing separate analysis.
Tapio classified decoupling states into eight categories based on decoupling index values (). Among these, strong decoupling represents the most desirable state, indicating economic growth coupled with reduced carbon emissions, while strong negative decoupling constitutes the worst scenario, reflecting economic decline accompanied by increased emissions.
Furthermore, when the decoupling state transitions from expansive negative decoupling → expansive connection → weak decoupling, it demonstrates continuous improvement in decoupling performance. Conversely, a transition from recessive decoupling → recessive connection → weak negative decoupling signifies progressive deterioration of decoupling conditions.
The specific classification criteria and categories of decoupling states are illustrated in Figure 2.
FIGURE 2
2.1.3 Dynamic mechanisms
The GTAP-E model employed in this study follows the recursive dynamic formulation of the GTAP dynamic framework (). Three mechanisms govern the temporal evolution from the 2017 base year through the 2023–2030 simulation period.
The model adopts static (myopic) expectations, in which investors take current-period rates of return as their best estimate of future rates of return. This convention is standard in recursively dynamic CGE models and avoids the computational complexity of forward-looking rational expectations. Under this formulation, agents do not anticipate future tariff shocks, and the model instead traces their impact through period-by-period adjustment dynamics.
The capital stock evolves according to the standard perpetual inventory method (Equation 6):where is the capital stock of region in period , is the region-specific depreciation rate (sourced from GTAP 11E capital stock data), and is new investment. Investment is allocated across sectors in proportion to sectoral rates of return: sectors earning above-average returns receive a larger share of new investment, following the mechanism described in . Capital is therefore imperfectly mobile across sectors in the short run but adjusts gradually over the simulation horizon. The speed of intersectoral capital reallocation is governed by a capital mobility parameter (, from the standard GTAP dynamic parameter set).
Total labor supply in each region follows the exogenous population and labor force projections reported in Supplementary Table A2. Skilled and unskilled labor are imperfectly substitutable (CES elasticity = 0.5). Labor is fully mobile across sectors within each region but immobile internationally. Real wages adjust to clear labor markets in each period. This closure implies that trade shocks affect the sectoral composition of employment without changing aggregate employment levels.
2.1.4 GTAP-E model and scenario setting
This study employs the Global Trade Analysis Project-Environmental (GTAP-E) model—a global network covering 141 economies and 65 sectors, to simulate the cross-regional impacts of PV trade barriers on carbon emissions and economic output through its endogenous bilateral trade and dynamic recursive structure (). Trade barriers in our scenarios include ordinary customs duties as well as trade remedy measures such as anti-dumping and countervailing duties, which are modeled as ad valorem tariff equivalents in GTAP-E. Drawing on current trade cases, three policy scenarios are defined:
The Business-as-Usual (BAU) scenario reflects the status quo as of 2021, including ordinary tariffs and non-tariff measures such as U.S. Section 201/301 tariffs and anti-dumping/countervailing duties on Chinese PV products (; ; ), India’s safeguard duties (), Turkey’s anti-dumping measures (), and Brazil’s Most-Favored-Nation tariffs. The BAU scenario captures the trade policy status quo as of 2021, which serves as the baseline against which we measure the additional impacts of the HTB and TTB scenarios. We use 2021 rather than a more recent year because 2021 represents the pre-escalation policy landscape before the major tariff increases announced in 2024–2025 (captured in our TTB scenario). Post-2021 policy changes are incorporated into the HTB and TTB scenarios, not into BAU.
The High Tariff Barrier (HTB) scenario extends BAU with elevated protectionism: U.S. tariffs on Chinese PV cells and modules rise to 50% (), China retaliates with 25% tariffs (), the U.S. revokes Southeast Asian exemptions, the EU reinstates anti-dumping/countervailing measures on all origins (), and Brazil, India, and Turkey maintain or expand existing barriers (; ; ).
The Trump Tariff Benchmark (TTB) scenario represents an aggressive policy shift: a 10% baseline tariff plus a 34% reciprocal tariff on Chinese PV products (; ; ), matched by Chinese tariffs on U.S. imports (), alongside broader reciprocal tariffs on multiple economies ().
Tariff rates are sourced from and , with non-tariff ad valorem equivalents from . Using the GTAP 11 E database (base year 2017) and multi-source projections from CEPII, IMF, and World Bank, core variables (capital, population, labor, GDP) are dynamically calibrated for 2017–2030 to enhance simulation accuracy. The cumulative change rates of variables across three phases are presented in Supplementary Table A2.
2.1.5 Uncertainty analysis
To assess model robustness and quantify uncertainty, sensitivity analysis is performed for all three scenarios (BAU, HTB, TTB) by perturbing two classes of uncertain parameters. First, the substitution elasticity of PV products (the elasticity of substitution between PV and non-PV electricity generation technologies) is varied by ±20% in the main text analysis and by ±50% in the Supplementary Material. Second, Armington elasticities, which govern the responsiveness of bilateral trade flows to tariff changes, are varied by ±50% across all sectors. These elasticities, sourced from the GTAP 11 E database (), are reported in full in Supplementary Table S7. The ±50% range on Armington elasticities is consistent with the econometric literature documenting that these parameters can vary by factors of 2–4 depending on estimation methodology (). A parameter sweep analysis further identifies the critical tariff threshold for embodied carbon rebound. The U.S. tariff on Chinese PV products is treated as a continuous variable, increased from 10% to 34% in 1% increments (24 simulations). For each tariff level, the annual change rate of embodied carbon emissions in both China’s and the U.S. PV sectors in 2025 is extracted. These results are used to construct a tariff-emission response curve, from which the critical threshold interval is determined.
2.2 Data
2.2.1 Data source
This study employs the EXIOBASE database as the primary data source, which offers two distinctive advantages: (1) fine-grained sectoral classification comprising 163 subsectors, with PV power generation explicitly listed as an independent sector; (2) a comprehensive environmental satellite account system providing key indicators including carbon emissions. In contrast, other mainstream multi-regional input-output tables (e.g., WIOD, GTAP, and Eora) aggregate the entire electricity sector without distinguishing renewable energy sources like PV. All monetary flow data were uniformly converted into million-euro units during the research process.
2.2.2 Data preprocessing
To accurately assess the independent impact of trade policies on the PV power generation sector, PV power generation must be separated from the electricity sector aggregated in GTAP. This study used power-generation data from the International Renewable Energy Agency (IRENA) as a bridge to achieve sectoral matching between EXIOBASE and GTAP (
). The key point is that the newly established PV electricity subsector directly adopted the carbon emission coefficients of the corresponding sector in the EXIOBASE MRIO database, ensuring that the high-carbon characteristics of PV manufacturing stages (e.g., polysilicon production) are accurately reflected in the policy simulations. Subsequently, within the GTAP-E framework, the trade flows and input–output structures of these new subsectors were calibrated. The specific steps are as follows:
Data preparation and calculation of PV power generation share
Obtain from the IRENA database the PV electricity generation (GWh) and total electricity generation (GWh) for each country/region in the target year (consistent with the GTAP base year 2017).
For each region
, calculate the share of PV power generation in total power generation (
) (
Equation 7):
2. Split GTAP power sector output
In the GTAP database, the total output of the power sector in each region r is .
Assuming that the output proportion of PV power relative to other electricity is identical to its proportion in electricity generation, the total output can be decomposed as:here, denotes the output of the PV power generation subsector (Equation 8), and denotes the output of the other electricity subsectors (Equation 9).
We acknowledge that using generation shares as a proxy for output shares assumes identical value-added per GWh across technologies. To assess the sensitivity of our results to this assumption, we conducted additional simulations using capacity shares and revenue shares as alternative disaggregation proxies. The critical tariff threshold and carbon rebound findings are robust across all three schemes (
Supplementary Section S1).
3. Bridge carbon emission factor
From the EXIOBASE MRIO model, we can obtain the direct carbon emission factor for the PV power generation sector in each region.
Core assumption: We assume that this carbon emission factor is transferable across databases. That is, the PV subsector disaggregated in the GTAP database adopts the carbon coefficient calculated from EXIOBASE (Equation 10):
For the “other electricity” sector, the carbon emission factor
is estimated from the difference between the total carbon emissions of GTAP’s original electricity sector and the carbon emissions of the PV subsector (
) (
Equations 11,
12).
4. Constructing new structures within the GTAP-E model
Replace the original electricity sector with two new sectors: PV power generation and other electricity. Assign to these two new sectors the output values calculated in Step 2 and the carbon emission factors calculated in Step 3. Split the original electricity trade flows, intermediate input structures, etc., proportionally by , or calibrate them according to relevant studies, to establish input–output linkages between the two new sectors and the rest of the economy. Ultimately, in the policy simulations, the tariff shock will be applied precisely only to the international trade flows of the PV power generation sector.
We note that U.S. solar tariffs legally apply to PV goods (modules and cells) rather than to electricity trade. In our model, the tariff shock on the PV power generation sector captures the downstream effects of higher equipment costs on deployment, as well as the upstream effects on manufacturing scale via the input-output linkages between the power sector and the manufacturing sectors that supply it. This is because the carbon intensity we assign to the PV power generation sector (48.3 g CO2/kWh) is derived from EXIOBASE’s input-output structure, which traces the full lifecycle embodied carbon from upstream manufacturing. When the PV power generation sector purchases PV modules as capital equipment, the carbon emissions from module manufacturing are attributed to the power generation sector through the Leontief inverse. Thus, a tariff on PV goods reduces export demand for Chinese-manufactured modules, which in turn affects the scale efficiency of the manufacturing supply chain embedded in the power sector’s carbon intensity. The tariff shock on the PV power generation sector is a necessary simplification driven by GTAP’s sectoral aggregation; our sensitivity analysis targeting upstream manufacturing proxies (Supplementary Section S2) confirms that this simplification does not drive the core findings.
3 Results
3.1 Historical analysis of sectoral output and embodied carbon emissions
The embodied carbon emissions, sectoral output value, and carbon intensity data of China’s PV power sector from 2000 to 2022 reflect distinctive industrial development characteristics (Figure 3). Embodied carbon emissions exhibited fluctuating growth overall, peaking during 2020–2021 before declining in 2022, correlating with industrial scale expansion, technological iteration, and policy cycles - emissions rise during expansion phases and fall during adjustment periods. Sectoral output value demonstrated long-term expansion, with explosive growth during 2019–2021 followed by a 2022 retreat, revealing an industrial trajectory from initial development to scale production, then to capacity optimization and demand transition.
FIGURE 3
Embodied carbon intensity displayed persistent long-term decline, confirming a clear decarbonization trend. During 2000–2010, carbon intensity dropped substantially due to technological breakthroughs; from 2010–2017, it rebounded owing to industrial expansion outpacing technological upgrades; post-2017 through 2022 saw sustained reduction, benefiting from renewed technological iteration and industrial maturity. Collectively, China’s PV sector has evolved through three phases: early-stage high-carbon inputs, mid-term carbon-reduction efficiency gains, and late-stage balanced scaling and technological advancement toward low-carbon efficiency. The carbon intensity reduction demonstrates technological upgrading efficacy under the “dual carbon” goals, while emission-output fluctuations mirror interactions between industrial cycles, policies, and market demand. Enhanced carbon efficiency in PV manufacturing provides critical support for energy system decarbonization.
3.2 Sectoral embodied carbon decoupling analysis
This subsection focuses on the decoupling relationship between output growth and embodied carbon emissions from PV equipment manufacturing–a novel perspective distinct from the conventional focus on PV’s role in displacing fossil electricity generation.
During 2000–2022, China’s PV power sector exhibited significant co-movement between output growth and embodied carbon emissions. Throughout the observation period, sectoral output expanded continuously while embodied emissions increased synchronously, demonstrating a stable positive correlation. Empirical data reveal that a 1% output growth drove a corresponding 0.5% rise in embodied emissions, with both growth rates remaining positive. This quantitative relationship indicates that although carbon emissions grew with industrial scaling, the carbon intensity per unit output showed a declining trend, reflecting an emerging decoupling effect during development.
Figure 4 presents the decoupling index evolution. The decoupling status underwent distinct phase transitions. From 2000 to 2004, the sector displayed expansive decoupling (DI > 1.2), where output and emissions grew simultaneously but emission growth significantly outpaced output growth–indicating reliance on relatively carbon-intensive modes during rapid expansion. Post-2005, the sector transitioned to weak decoupling, maintaining co-directional growth of output and emissions but with fluctuating narrowing of their growth differentials.
FIGURE 4
Specifically, the decoupling index declined during 2005–2010, reflecting a widening gap between emission and output growth rates. From 2010 to 2016, the index rebounded, suggesting accelerated emission growth relative to output. During 2016–2022, the index declined again, signaling renewed convergence between emission and output growth rates. This trajectory illustrates how the PV sector progressively promoted relative decoupling of economic growth from carbon emissions over its 23-year development, achieving dynamic balance between scale expansion and low-carbon transition through continuous adjustments and optimization.
The historical decoupling pattern documented here provides the baseline against which we simulate the disruption caused by U.S. tariffs in the following section.
3.3 Impact of trade barriers on China’s PV power sector
The simulation results reveal a significant suppressive effect of tariff shocks on China’s PV output, with the severity varying by policy intensity. Under the BAU scenario, China’s PV power sector demonstrates robust growth momentum, maintaining an average annual output growth rate of 11.5% during 2023–2030, with cumulative expansion reaching 1.58 times the 2022 output level by 2030 (i.e., a 58% increase from the 2022 baseline, Supplementary Table A3 and Figure 5). This growth primarily stems from the continuous advancement of domestic “dual carbon” goals and the cushioning effect of Southeast Asian transit trade, which contributes 35% of total exports.
FIGURE 5
However, the HTB scenario significantly suppresses this growth trajectory. Post-2025, the annual growth rate plummets to 4.3%, with cumulative growth reaching only 53% of the BAU level. This is principally attributable to a 62% decline in U.S. exports due to increased tariffs to 50%, coupled with the European market share shrinking from 28% to 15% following the EU’s reinstatement of anti-dumping measures.
The most extreme TTB scenario exhibits a deep V-shaped fluctuation. Output declines by 2.1% in the policy implementation year (2025) and further drops by 3.8% in the subsequent year. This stems from a 44% compound tariff effectively closing the U.S. market (exports plummeting by 92%) and disruptions in Southeast Asian supply chains. Notably, a gradual recovery emerges post-2027 under TTB, indicating Chinese firms’ adaptive restructuring through rebalancing toward Asian-African-Latin American markets (61% share by 2030) and vertical integration strategies.
The price transmission mechanism exhibits significant differences across the three scenarios. Under BAU, prices remain stable with an annual increase of merely 1.3%, reflecting mature supply chain cost control. In contrast, the HTB scenario shows an 8.1% year-on-year price surge in 2025, driven by tariff cost amplification—the model indicates that a 50% statutory tariff actually elevates end-user prices by 28%. The TTB scenario demonstrates characteristic policy shock spikes: prices skyrocket 15%–18% during 2025–2026, far exceeding the tariff magnitude. Although prices moderate post-2027, they persistently exceed BAU benchmarks, evidencing permanent efficiency losses from trade barriers that manifest as long-term cost stickiness during industrial chain restructuring.
Carbon emission trajectories reveal a complex environmental paradox. BAU achieves continuous improvement (annual 5.7% reduction) through dual drivers of technological advancement and energy structure optimization. HTB not only suppresses output growth but also slows decarbonization (annual 3.4% decline), primarily due to weakened scale effects reducing corporate emission-cutting investments. Most alarmingly, TTB triggers a 2025–2027 carbon rebound (cumulative 4.9% increase) through three mechanisms: temporary resurgence in power generation carbon intensity from coal-fired backup capacity activation, extended logistics distances for South American markets, and efficiency losses from fragmented small-scale production.
Figure 6 presents the expected rates of change in embodied carbon emissions of China’s and the U.S. PV sectors under different tariff levels. For both countries, the relationship is nonlinear with a notable change in slope in the 18%–19% range. Below this range, the rate of change for China remains negative (emissions still declining) while the U.S. rate is positive but modest. As tariffs enter the 18%–19% range, both curves cross into more pronounced positive territory–China from negative to positive, the U.S. from modest to steep increase.
FIGURE 6
The underlying mechanisms differ across the two countries (see Supplementary Section S4.1 for a full decomposition). In the U.S., the 18%–19% range is where the tariff-inclusive price of Chinese PV crosses the threshold at which domestic production becomes cheaper than Chinese imports for a critical mass of the market, triggering a discrete jump in import substitution toward the much more carbon-intensive U.S. domestic supply (the “composition effect”). In China, the same tariff range triggers a scale efficiency discontinuity: the collapse of U.S. export demand pushes manufacturers below minimum efficient scale for certain supply chain segments, activating carbon-intensive backup capacity and forcing logistics restructuring.
We refer to the 18%–19% range as the “critical tariff threshold” (shaded area in the figure). It represents the tariff level at which supply chain fragmentation crosses from marginal to structural in both countries, albeit through different channels. The Chinese response is smoother (crossing gradually from negative to positive) while the U.S. response is sharper (a more discrete jump), consistent with the different mechanisms at work.
The apparent nonlinearity at the 18%–19% tariff level arises from the interaction between the GTAP-E Armington trade structure and sectoral closure rules. Below this threshold, the technique effect, driven by ongoing technological progress and declining carbon intensity, dominates, enabling continued emission reductions despite moderate trade disruption. Above the threshold, a composition effect is abruptly activated: U.S. import demand for Chinese PV products collapses when tariff-inclusive prices exceed the CES substitution threshold, triggering simultaneous supply-chain fragmentation in China (coal-fired backup activation, loss of minimum efficient scale) and high-carbon import substitution in the U.S. (replacement of low-carbon Chinese PV with more carbon-intensive domestic and third-country production). A full decomposition of emission changes into scale, composition, and technique effects across all tariff levels, confirming this regime-switch mechanism, is provided in the Supplementary Section S4.
The study’s cross-regional comparative analysis of Chinese and U.S. PV sectors under BAU/HTB/TTB scenarios reveals asymmetric impacts. Regarding output, China demonstrates stronger growth resilience under BAU (annual 11.5%) versus U.S. (5.2%–5.9%), benefiting from integrated supply chains and economies of scale. When facing TTB’s extreme tariffs, China’s 2025 output decline (2.1%) proves milder than the U.S.'s 7.8%, highlighting China’s superior buffering capacity through Asian-African-Latin American market diversification (61% share by 2030) compared to U.S. reliance on domestic production vulnerability. Notably, HTB triggers opposing reactions: China’s growth plummets to 4.3% post-2025 due to export constraints, while the U.S. experiences a 12.3% short-term output surge from import substitution, validating trade protection’s redistribution effects on global PV industries. Readers may find it counterintuitive that Chinese PV modules have lower embodied carbon than U.S. domestic production. This is well-established in the life cycle assessment (LCA) literature. China’s PV manufacturing benefits from economies of scale, more integrated supply chains, and–critically–a lower carbon intensity of polysilicon production (; ). report harmonized LCA estimates of 40–55 g CO2/kWh for Chinese utility-scale PV versus 70–100 g CO2/kWh for U.S. manufacturing. The U.S. domestic carbon intensity of 87.4 g CO2/kWh in our model falls within this range. This differential is the fundamental driver of the U.S. carbon surge: when tariffs force substitution away from Chinese imports, the replacement supply (whether domestic U.S. or third-country) has substantially higher embodied carbon. Carbon emission interactions appear more intricate: China’s superior annual reduction under BAU (5.7% vs. U.S. 4.3%) reflects manufacturing efficiency leadership, while TTB’s environmental backfire proves drastically worse in the U.S. (2025–2027 cumulative increases: China 4.9% vs. U.S. 30.5%), rooted in industrial structure disparities. All of the above results have passed sensitivity tests and are robust.
The asymmetry in carbon rebound magnitudes reflects structural differences: China’s 4.9% increase is driven primarily by efficiency losses (fragmented production scales, logistics reconfiguration), whereas the U.S. 30.5% surge arises from a supply composition shock—the forced substitution of low-carbon Chinese PV imports with higher-carbon domestic and third-country alternatives (see Supplementary Section S4 for bilateral trade flow decomposition and carbon intensity differentials).
The recalculated results based on the 2022 benchmark year reveal significant scenario-dependent variations in the decoupling status of China’s PV power sector (Figure 7). Under both BAU and HTB scenarios, the sector’s economic growth and embodied carbon emissions transition to a strong decoupling relationship during 2023–2030. Notably, the HTB scenario demonstrates a year-on-year declining decoupling index, indicating that external trade pressures have driven more pronounced low-carbon transition through technological upgrading and energy structure optimization. The TTB scenario exhibits a unique “V-shaped” evolutionary trajectory: the 2025 policy shock temporarily degrades decoupling status to weak decoupling, with a sharp rebound in the decoupling index, reflecting transient disruptions to emission reduction progress caused by supply chain fractures. However, as industrial adaptive adjustments deepen, the decoupling index re-enters a downward trajectory post-2028, projecting restoration to strong decoupling status around 2030.
FIGURE 7
4 Conclusion and discussion
This study couples a MRIO model, the Tapio decoupling model, and the GTAP-E model to systematically evaluate the climate costs of U.S. tariffs on Chinese PV under different scenarios during 2000–2030. The main conclusions confirm that protectionist trade policies not only induce losses in economic efficiency but also exert far-reaching and complex negative environmental feedbacks on the global clean energy transition; in particular, when tariff intensity exceeds approximately 18%–19%, supply chain fragmentation in both countries crosses from marginal to structural, triggering a tariff-induced carbon rebound in China’s manufacturing sector and a sharp acceleration of emissions in the U.S. PV market. We term this the “critical tariff threshold” – not a sharp reversal, but the range in which composition effects begin to dominate ongoing efficiency improvements.
4.1 Key research findings and theoretical implications
The core finding of this study lies in revealing the nonlinear relationship among trade policy, industrial dynamics, and carbon emissions. Historical analysis (2000–2022) shows that during large-scale development, China’s PV industry has formed a pronounced decoupling trend, whereby a 1% increase in output drives only a 0.5% increase in embodied carbon emissions, reflecting the decarbonization contribution of technological progress. In the early stage (2000–2004), however, during the expansive decoupling phase, the growth rate of carbon emissions far outpaced that of output (DI > 1.2), which is consistent with ’s “environmental cost hypothesis of early-stage clean-technology industries,” indicating that emerging industries at the initial scale-up stage often face technological lock-in effects and rigid energy structure constraints.
Policy simulations (2023–2030) further reveal that this decarbonization trajectory is highly susceptible to disruption by external trade shocks. Under the BAU scenario, the sector’s sustained strong decoupling corroborates ’s assertion regarding the “endogenous decarbonization mechanism” of the PV industry. However, the scenario comparison exposes stark asymmetries that carry significant theoretical implications.
Divergent environmental outcomes across tariff intensities. Our findings reveal a fundamental distinction between the environmental consequences of moderate versus extreme tariff pressures. Under the HTB scenario (moderate tariffs), China’s PV sector maintains strong decoupling throughout 2023–2030, with a declining decoupling index suggesting that moderate external pressure does not reverse decarbonization progress. In sharp contrast, the TTB scenario (extreme tariffs, 44% compound rate) triggers a 4.9% cumulative embodied carbon rebound in China’s PV sector and a 30.5% surge in U.S. PV emissions during 2025–2027. This divergence stems from the fundamental asymmetry in how the PV supply chain responds to different tariff levels: below the 18%–19% threshold, technological progress and market diversification absorb the shock; above the threshold, supply chain fragmentation, logistics inefficiencies, and the activation of carbon-intensive backup capacity overwhelm the sector’s adaptive mechanisms, reversing emissions from decline to growth. We term this the “tariff-induced carbon rebound.”
Our identification of a critical tariff threshold (18%–19%) extends prior PV trade research in two important ways. First, demonstrated that anti-dumping duties on Chinese PV products primarily redistribute trade flows to third markets rather than eliminate them. Our simulations confirm this trade-diversion effect—China’s market share pivots toward Asian-African-Latin American markets (61% by 2030 under TTB)—but additionally quantify its environmental cost: extended logistics distances and fragmented production scales generate a 4.9% carbon rebound that purely trade-focused studies did not capture. Second, while established a linear relationship between trade openness and global carbon mitigation, and used social network analysis to show that countries with more central positions in the global PV trade network tend to have lower carbon emission intensities in their PV sectors—suggesting that trade integration facilitates emission reductions through knowledge spillovers and scale economies—our sector-level dynamic framework reveals a nonlinear discontinuity. Moderate tariffs (below 18%) are partially absorbed by technological progress and market diversification without triggering an absolute carbon rebound. However, once tariffs exceed the 18%–19% threshold, supply chain fragmentation, logistics inefficiencies, and the activation of carbon-intensive backup capacity overwhelm the sector’s adaptive mechanisms, reversing emissions from decline to growth. This threshold behavior is not detectable in macro-level integrated assessment models or network-based approaches, which typically smooth over the granular supply-chain dynamics that our coupled MRIO-Tapio-GTAP-E framework captures.
The comparison between HTB and TTB scenarios reveals a critical nonlinearity: moderate tariff pressure (HTB) does not reverse China’s decarbonization progress, as the sector maintains strong decoupling throughout 2023–2030. However, extreme tariff pressure (TTB) degrades the decoupling status to weak decoupling in 2025, suspending emission reduction progress for three consecutive years. This suggests that the PV sector’s adaptive capacity operates within a bounded range of external pressure; beyond the 18%–19% threshold identified in our analysis, supply chain fragmentation and logistics inefficiencies overwhelm the sector’s ability to compensate through technological upgrading or market diversification. Within the GTAP-E framework, our study also captures the market-power rivalry between China and the United States as major PV players. The simulation results show that U.S. tariffs are not borne entirely by Chinese exporters but are partially passed on to U.S. consumers through global price mechanisms (end-user prices surge 15%–18% under TTB). Meanwhile, China partially offsets the trade shock through market diversification, demonstrating adjustment resilience. However, this adaptive adjustment entails a substantial climate cost: under the TTB scenario, supply chain restructuring leads to the 4.9% cumulative rebound in China’s embodied carbon emissions, while the U.S., relying on more carbon-intensive domestic production, experiences a 30.5% cumulative increase over the same period. This finding refines the applicability boundary of the Porter hypothesis in renewable energy trade and provides empirical confirmation that, in highly globalized industries, unilateral trade barriers exacerbate the predicament of global climate governance through carbon leakage effects.
4.2 Policy implications: from wish lists to evidence-based design
Based on the above findings, we propose the following more targeted and feasible policy recommendations.
First, recognize that undifferentiated punitive tariffs on clean energy equipment generate quantifiable environmental backfire effects. Our simulations show that the TTB scenario (44% compound tariff) produces a lose-lose outcome: China’s PV embodied carbon rebounds by 4.9% while U.S. emissions surge by 30.5% during 2025–2027. Policymakers should therefore weigh the climate costs of such measures alongside their stated trade objectives. Rather than applying across-the-board punitive tariffs, trade measures could be designed to differentiate based on the product lifecycle carbon footprint, with lower barriers for certified low-carbon PV modules. Such an approach would shift trade policy from a purely punitive tool toward one that incentivizes green competition, without conflating tariffs with carbon-pricing instruments like CBAM, which serve fundamentally different purposes.
Second, strengthen the coordination between domestic industrial resilience and technological breakthroughs. The deceleration of carbon-reduction speed under the HTB scenario (average annual decline of 3.4% vs. 5.7% under BAU) indicates that maintaining necessary economies of scale is the foundation for sustained decarbonization. Policy should safeguard core capacity utilization through dedicated financing instruments and establish special programs for breakthroughs in zero-carbon production technologies, focusing on green-power substitution for high-energy-consuming stages such as silicon smelting. As South Korea’s investment in PV forecasting infrastructure illustrates (), technology-driven resilience creates durable competitive advantages, whereas tariff-driven protection generates only transient gains—a lesson directly applicable to China’s current trade environment.
Third, deploy a “green diversification” supply chain strategy. Our GTAP-E simulation shows that under the TTB scenario, China’s PV exports pivot substantially toward Asian-African-Latin American markets (61% share by 2030), demonstrating that market diversification functions as an effective risk-hedging mechanism against unilateral tariff shocks. However, the carbon rebound observed in the same scenario (+4.9% during 2025–2027) also warns that unmanaged capacity relocation carries environmental risks—a finding that is further corroborated by Taiwan’s experience with distributed rooftop PV, where decentralized deployment successfully reduced trade exposure while advancing domestic decarbonization. Building on these model-supported findings, we recommend, as a policy inference beyond the direct scope of our quantitative simulations, that when China establishes production capacity in regions such as Southeast Asia and Central and Eastern Europe, these investments should be positioned as “green industrial clusters” with explicit mandates for low-carbon technology deployment. This would involve coupling outward direct investment with the export of China’s advanced PV manufacturing technologies and green-electricity solutions, thereby proactively mitigating the carbon leakage risk that the model identifies but does not endogenously resolve.
Fourth, embed trade policy within regional cooperative frameworks. show that China, Japan, and South Korea possess complementary strengths in PV manufacturing under the RCEP framework. Building on this, China could pursue a plurilateral “Clean Energy Trade Facilitation Agreement” to establish mutual recognition of carbon-footprint certification. Such regional cooperation would transform zero-sum tariff dynamics into positive-sum coordination, converting geopolitical risks into opportunities for collective low-carbon transition.
4.3 Research limitations and future prospects
This study has several limitations that suggest directions for future research. First, the quantification of non-tariff barriers in GTAP-E relies on ad valorem tariff equivalents, which cannot fully capture the heterogeneous impacts of technical barriers such as certification requirements. Second, the sectoral disaggregation procedure uses generation shares as a proportionality proxy between physical output and economic value; although sensitivity tests using capacity and revenue shares confirm robustness, future model versions with finer PV manufacturing sectoral resolution would enable more precise mapping of trade policy instruments to their direct targets. Third, due to GTAP’s sectoral aggregation, we apply the tariff shock to the PV power generation sector rather than directly to PV goods manufacturing sectors. This simplification is necessary because GTAP 11 E does not disaggregate PV component manufacturing (polysilicon, wafers, cells, modules) as separate sectors. To our knowledge, no publicly available global CGE database currently offers such disaggregation. GTAP 11 E aggregates all “electronic equipment” into a single sector (ISIC 26–27), which includes PV components alongside computers, semiconductors, and consumer electronics. EXIOBASE has finer sectoral resolution but lacks the GTAP-E energy substitution and trade structure needed for tariff simulation. Our approach–disaggregating the electricity sector while applying tariff shocks to upstream manufacturing proxies (Supplementary Section S2) – represents a feasible second-best solution given current data constraints. Our sensitivity analysis confirms that this simplification does not drive the core qualitative findings. Fourth, the recursive dynamic structure employs static expectations and standard closure rules; structural uncertainty, including alternative labor market closures, investment allocation rules, and forward-looking expectations, remains unexplored and would require fundamentally different model architectures. Fifth, the model does not endogenously capture technology transfer dynamics or environmental governance responses to capacity relocation, dimensions central to the green diversification recommendation. Sixth, the critical tariff threshold (18%–19%), while robust across the extensive sensitivity tests, should be interpreted as model-conditional; empirical validation using ex-post trade and emissions data would strengthen confidence in the specific numerical estimate. More broadly, future research should move beyond treating all trade barriers as analytically equivalent and instead develop typological frameworks that distinguish carbon-pricing measures from punitive tariffs, moderate pressures from extreme shocks, and linear deployment effects from nonlinear sectoral decarbonization responses.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
Author contributions
HY: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Writing – original draft. SZ: Resources, Software, Supervision, Validation, Visualization, Writing – review and editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was supported in part by the Beijing Wuzi University Youth Scientific Research Fund Sponsorship (2024XJQN21), the National Natural Science Foundation of China (62502038) and the Fundamental Research Funds for Beijing Municipal Universities (2025JKZX06).
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2026.1835650/full#supplementary-material
References
1
CaiH.LiaoZ.LiT. (2025). Impacts of RCEP’s trade barrier reductions on China’s agricultural trade: a GTAP simulation. PloS One20 (7), e0328060. 10.1371/journal.pone.0328060
2
ChenS. (2024). The impact of green trade barriers on China’s PV products exports to ASEAN. Front. Environ. Sci.12, 1459950. 10.3389/fenvs.2024.1459950
3
DuanH.ZhangG.FanY.WangS. (2017). Role of endogenous energy efficiency improvement in global climate change mitigation. Energy Effic.10 (2), 459–473. 10.1007/s12053-016-9468-1
4
European (2020). Investigations history of proceeding-solar panels (crystalline silicon PV modules and key components). Available online at: https://trade.ec.europa.eu/tdi/case_history.cfm?ref=com&init=1895&sta=1&en=20&page=1&number=&prod=module&code=&scountry=all&proceed=all&status=all&measures=all&measure_type=all&search=ok&c_order=name&c_order_dir=Up.
5
FerrierG. D.ReyesJ.ZhuZ. (2016). Technology diffusion on the international trade network. J. Public Econ. Theory18 (2), 291–312. 10.1111/jpet.12186
6
FrischknechtR.HeathG.RaugeiM.SinhaP.de Wild-ScholtenM.FthenakisV.et al (2016). “Methodology guidelines on life cycle assessment of photovoltaic electricity,” in IEA PVPS Task 12, Report T12-08:2016. 3rd edn. International Energy Agency Photovoltaic Power Systems Programme.
7
GaoS.SongZ. (2025). Trade frictions on China's PV trade and their reshape effects. Renew. Energy244, 122708. 10.1016/j.renene.2025.122708
8
GerardenT. D.BollingerB.GillinghamK.XuD. Y. (2025). Strategic avoidance and the welfare impacts of U.S. solar panel tariffs (NBER Working Paper No. w34401)National Bureau of Economic Research. 10.3386/w34401
9
GolubA. A.HertelT. W. (2012). Modeling land-use change impacts of biofuels in the GTAP-BIO framework. Clim. Change Econ.3 (03), 1250015. 10.1142/s2010007812500157
10
GuoC. Y.ChenC. (2025). Analyzing drivers of embodied carbon in renewable energy sector within global industry chains. Clim. Change Res.21 (6), 789–806.
11
GuoQ.YouW. (2025). The trade–environment nexus in global solar photovoltaic product trade: mechanisms and policy implications. J. Renew. Sustain. Energy17 (3), 033505. 10.1063/5.0264415
12
GuoQ.WenJ.ChenG. (2023). Comprehensive evaluation of the international competitiveness of solar photovoltaic products in China, Japan, and Korea under RCEP background. Environ. Sci. Pollut. Res.30 (56), 118440–118455. 10.1007/s11356-023-30599-1
13
HelvestonJ. P.HeG.DavidsonM. R. (2022). Quantifying the cost savings of global solar PV supply chains. Nature612 (7938), 83–87. 10.1038/s41586-022-05316-6
14
HertelT. W.HummelsD.IvanicM.KeeneyR. (2007). How confident can we be of CGE-Based assessments of free trade agreements?Econ. Model.24 (4), 611–635. 10.1016/j.econmod.2006.12.002
15
HughesL.MecklingJ. (2017). The politics of renewable energy trade: the US-China solar dispute. Energy Policy105, 256–262. 10.1016/j.enpol.2017.02.044
16
IanchovichinaE.WalmsleyT. L. (2012). Dynamic Modeling and Applications for Global Economic Analysis. Cambridge University Press.
17
ITA (2025). Final affirmative determinations in the antidumping and countervailing duty investigations of crystalline PV cells whether or not assembled into modules from cambodia, malaysia, thailand, and the socialist republic of vietnam. Available online at: https://www.trade.gov/final-affirmative-determinations-antidumping-and-countervailing-duty-investigations-crystalline.
18
JungY.JungJ.KimB.HanS. (2020). Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar PV facilities: case study of South Korea. J. Clean. Prod.250, 119476. 10.1016/j.jclepro.2019.119476
19
Kutlina-DimitrovaZ.AntimianiA. (2019). Armington Elasticities in CGE Models: A Sensitivity Analysis. JRC Tech. Rep. European Commission, Joint Research Centre.
20
LinB.LiuY. (2024). Global carbon neutrality and China's contribution: the impact of international carbon market policies on China's PV product exports. Energy Policy193, 114299. 10.1016/j.enpol.2024.114299
21
LinB.LiuY. (2025). Renewable energy trade destruction and deflection: effects of antidumping and countervailing measures on Chinese solar photovoltaic exports. Energy Policy204, 114687. 10.1016/j.enpol.2025.114687
22
MOFCOM (2020). Foreign Trade Online Inquiry. Available online at: http://wmsw.mofcom.gov.cn/wmsw/.
23
MOFCOM (2023). Brazil decides to impose import duties on PV module products. Available online at: http://br.mofcom.gov.cn/article/jmxw/202312/20231203461292.shtml.
24
MOFCOM (2024). Turkey makes final anti-circumvention ruling in anti-dumping case against China’s PV modules. Available online at: https://www.resmigazete.gov.tr/eskiler/2024/03/20240319-2.htm.
25
MOFCOM (2025). Commission of the State Council on Adjusting the Additional Tariff Measures on Imported Goods Originating in the United States. Available online at: https://gss.mof.gov.cn/gzdt/zhengcefabu/202505/t20250513_3963684.htm.
26
NiuZ.LiuC.GunesseeS.MilnerC. (2018). Non-tariff and overall protection: evidence across countries and over time. Rev. World Econ.154 (4), 675–703. 10.1007/s10290-018-0317-5
27
PantH. (2015). “A generic approach to investment allocation in recursively dynamic CGE models,” in GTAP Resource #4825. ANU, Canberra: Crawford School of Public Policy.
28
PetersJ. C. (2016). The GTAP-Power data base: disaggregating the electricity sector in the GTAP data base. J. Glob. Econ. Analysis1 (1), 209–250. 10.21642/jgea.010104af
29
RahdanP.ZeyenE.VictoriaM. (2025). Strategic deployment of solar PV for achieving self-sufficiency in Europe throughout the energy transition. Nat. Commun.16 (1), 6259. 10.1038/s41467-025-61492-9
30
ShenT.CaiY.LiuL. (2025). Green supply chain with strategic retailers: transportation-Based carbon penalty. Energy Econ.145, 108411. 10.1016/j.eneco.2025.108411
31
TapioP. (2005). Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001. Transp. Policy12 (2), 137–151. 10.1016/j.tranpol.2005.01.001
32
USITC (2012). Import injury investigation-crystalline silicon PV cells and modules from china. Available online at: https://www.usitc.gov/investigations/701731/2012/crystalline_silicon_PV_cells_and_m odules/final.htm.
33
USITC (2014). Import injury investigatons-certain crystalline silicon PV products from china and taiwan. Available online at: https://www.usitc.gov/investigations/701731/2014/certain_crystalline_silicon_PV_products/final.htm.
34
USTR (2020). China section 301-tariff actions and exclusion process. Available online at: https://ustr.gov/issue-areas/enforcement/section-301-investigations/tariff-actions.
35
USTR (2024). U.S. Trade Representative Katherine Tai to Take Further Action on China Tariffs After Releasing Statutory Four-Year Review. Available online at: https://ustr.gov/about-us/policy-offices/press-office/press-releases/2024/may/us-trade-representative-katherine-tai-take-further-action-china-tariffs-after-releasing-statutory.
36
WangQ.HanX. (2021). Is decoupling embodied carbon emissions from economic output in Sino-US trade possible?Technol. Forecast. Soc. Change169, 120805. 10.1016/j.techfore.2021.120805
37
WangM.MaoX.XingY.LuJ.SongP.LiuZ.et al (2021). Breaking Down barriers on PV trade will facilitate global carbon mitigation. Nat. Communications12 (1), 6820. 10.1038/s41467-021-26547-7
38
WangW.HoudeS. (2026). The incidence of the US-China solar trade warJ. Associat. Environ. Res. Econ.13 (1), 41–75. 10.1086/736763
39
WH (2025a). Amendment to reciprocal tariffs and updated duties as applied to low-value imports from the people’s republic of china. Available online at: https://www.whitehouse.gov/presidential-actions/2025/04/amendment-to-recipricol-tariffs-and-updated-duties-as-applied-to-low-value-imports-from-the-peoples-republic-of-china/.
40
WH (2025b). Clarification of Exceptions Under Executive Order 14257 of April 2, 2025, as Amended. Available online at: https://www.whitehouse.gov/presidential-actions/2025/04/clarification-of-exceptions-under-executive-order-14257-of-april-2-2025-as-amended/
41
WH (2025c). Modifying reciprocal tariff rates to reflect discussions with the people’s republic of china. Available online at: https://www.whitehouse.gov/presidential-actions/2025/05/modifying-reciprocal-tariff-rates-to-reflect-discussions-with-the-peoples-republic-of-china/.
42
WTO (2020). Tariff analysis online facility. Available online at: https://tao.wto.org/welcome.aspx?ReturnUrl=%2f%3fui%3d1&ui=1.
43
YueD.YouF.DarlingS. B. (2014). Domestic and overseas manufacturing scenarios of silicon-based PVs: life cycle energy and environmental comparative analysis. Sol. Energy105, 669–678. 10.1016/j.solener.2014.04.008
44
ZhengS.KahnM. E. (2017). A new era of pollution progress in urban China?J. Econ. Perspect.31 (1), 71–92. 10.1257/jep.31.1.71
Summary
Keywords
decoupling analysis, embodied carbon emissions, GTAP-E model, photovoltaic power, trade barriers
Citation
Yuan H and Zheng S (2026) The clean energy trade war: assessing the climate costs of U.S. solar tariffs on China’s PV sector. Front. Environ. Sci. 14:1835650. doi: 10.3389/fenvs.2026.1835650
Received
21 March 2026
Revised
23 May 2026
Accepted
21 July 2026
Published
14 August 2026
Volume
14 - 2026
Edited by
Jin Hu, Fudan University, China
Reviewed by
Xinjiletu Yang, Inner Mongolia University of Technology, China
Yu-You Liou, National Taiwan University, Taiwan
Updates
Copyright
© 2026 Yuan and Zheng.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Shuxian Zheng, zhengsx@mail.bnu.edu.cn
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.