In recent years, the global shift toward sustainable energy and environmental protection has accelerated, making the development of green, low-carbon industries a priority. Among these, the electric car sector has emerged as a key driver of economic transformation. As a leading player in this field, China has witnessed rapid growth in its electric car exports, particularly within the framework of the Regional Comprehensive Economic Partnership (RCEP). This article analyzes the international competitiveness of China’s electric car industry compared to other RCEP members, using trade data from 2013 to 2022. I employ indices such as International Market Share (IMS), Revealed Comparative Advantage (RCA), Trade Competitiveness (TC), and the entropy method for a comprehensive evaluation. Furthermore, I utilize a fixed-effects panel data model to identify factors influencing China’s electric car exports to RCEP countries. The findings indicate that China’s electric car exports have expanded significantly, with ASEAN, South Korea, and Japan being primary markets. China, South Korea, and Japan demonstrate strong competitive growth, while Australia and New Zealand lag, and ASEAN nations show potential. Key influencers include the GDP of importing countries, China’s R&D expenditure in automobile manufacturing, China’s GDP, and the annual number of automobile patent grants in China. Based on this, I propose strategies to enhance market adaptability, diversify markets, optimize R&D resource allocation, improve innovation conversion efficiency, strengthen the electric car supply chain, and leverage RCEP rules for high-quality development.
The global urgency for energy transition and climate action has propelled the electric car industry to the forefront of sustainable development. As nations strive to meet carbon neutrality goals, electric vehicles (EVs) have become a critical component of green economic policies. In this context, China’s electric car sector, often referred to as China EV, has achieved remarkable progress, driven by substantial manufacturing capabilities and policy support. For instance, China’s commitment to “dual carbon” targets underscores its dedication to reducing emissions through innovations in the electric car market. However, despite global expansion, China’s electric car exports face challenges in Western markets due to trade barriers and intense competition. The signing of RCEP, however, presents new opportunities by facilitating trade among member countries, which include diverse economies with growing demand for electric cars. This article delves into the competitiveness dynamics within RCEP, focusing on how China’s electric car industry can leverage this agreement for sustained growth. By examining trade patterns and influencing factors, I aim to provide insights that support strategic decision-making for China’s electric car exports.
Existing research on electric car trade often highlights the role of economic factors and policy frameworks. For example, studies have used social network analysis to explore trade structures in regions like the Belt and Road Initiative, emphasizing factors such as economic levels, tariffs, and battery trade. Others have applied stochastic frontier gravity models to assess export efficiency, noting the impact of GDP, population, and shared language. However, there is a gap in comprehensive analyses within the RCEP framework, particularly using integrated competitiveness indices. This article addresses this by combining multiple metrics and empirical modeling to evaluate China’s electric car standing relative to RCEP partners. The entropy method, in particular, allows for an objective weighting of competitiveness indicators, reducing subjectivity in assessments. Moreover, the fixed-effects model controls for unobserved heterogeneity, providing robust estimates of export determinants. Through this approach, I contribute to a deeper understanding of how China’s electric car industry can navigate the evolving global trade landscape.
To assess the export landscape, I first examine the scale of China’s electric car trade with RCEP members. Electric cars are defined under HS codes 870220-870240, 870340-870380, and 870911, covering various types of electric vehicles. Data from UN Comtrade reveals that China’s electric car exports to RCEP countries have surged from a modest base in 2016 to over $3 billion in 2022, accounting for 13% of China’s total electric car exports. This growth underscores the increasing importance of RCEP markets for China EV expansion. In contrast, exports to major Western markets, such as the EU and US, have faced headwinds due to tariff hikes and technical standards, leading to a decline in their share of China’s electric car exports from 65.83% in 2021 to 50.61% in 2022. This shift highlights the strategic value of RCEP in diversifying China’s electric car export destinations.
Market distribution within RCEP shows that ASEAN and South Korea are the primary destinations for China’s electric car exports, followed by Australia and Japan. For instance, Australia has emerged as the largest market within RCEP, capturing 40% of China’s electric car exports to the region in 2022, while New Zealand saw a fivefold increase. However, ASEAN’s share declined from 59.80% in 2016 to 20.70% in 2022, possibly due to growing local production and competition. Japan, despite its advanced automotive industry, has seen fluctuating import levels from China. These trends suggest that China’s electric car exports are adapting to regional demands, but further analysis is needed to understand the underlying competitiveness.
To measure international competitiveness, I employ three key indices: International Market Share (IMS), Revealed Comparative Advantage (RCA), and Trade Competitiveness (TC). The IMS index is calculated as follows: $$IMS_{ij} = \frac{X_{ij}}{X_{wj}} \times 100\%$$ where \(X_{ij}\) represents the export value of electric cars from country i to market j, and \(X_{wj}\) is the world’s total exports to market j. A higher IMS indicates a stronger presence in the global electric car market. For China, the IMS for electric cars increased from 7.35% in 2013 to 10.21% in 2022, reflecting growing global influence. In comparison, Japan’s IMS peaked at 31.60% in 2017 but declined to 10.04% by 2022, while South Korea maintained a stable IMS around 6-8%. Australia and New Zealand had minimal IMS values, below 1%, indicating limited market share.
| Year | China | Japan | South Korea | Australia | New Zealand | ASEAN |
|---|---|---|---|---|---|---|
| 2013 | 7.35 | 6.07 | 1.05 | 0.66 | 0.01 | 4.38 |
| 2014 | 5.03 | 5.84 | 0.98 | 0.48 | 0.00 | 3.43 |
| 2015 | 6.22 | 5.99 | 1.18 | 0.48 | 0.00 | 0.42 |
| 2016 | 8.72 | 6.99 | 1.01 | 1.32 | 0.02 | 0.61 |
| 2017 | 0.82 | 31.60 | 8.73 | 0.02 | 0.01 | 1.01 |
| 2018 | 1.03 | 26.81 | 8.05 | 0.10 | 0.00 | 0.92 |
| 2019 | 1.74 | 23.81 | 7.36 | 0.04 | 0.01 | 0.33 |
| 2020 | 2.74 | 17.53 | 5.90 | 0.02 | 0.00 | 0.70 |
| 2021 | 5.66 | 12.32 | 6.06 | 0.01 | 0.00 | 0.48 |
| 2022 | 10.21 | 10.04 | 6.78 | 0.01 | 0.00 | 0.36 |
The RCA index assesses comparative advantage by comparing a country’s export share of electric cars to its total exports relative to the world average. It is defined as: $$RCA_{ij} = \frac{(X_{ij} / X_{it})}{(X_{wj} / X_{wt})}$$ where \(X_{ij}\) is the export value of electric cars from country i, \(X_{it}\) is total exports of country i, \(X_{wj}\) is world exports of electric cars, and \(X_{wt}\) is total world exports. An RCA greater than 1 indicates a comparative advantage. For China, the RCA for electric cars remained below 0.8 throughout 2013-2022, suggesting weaker comparative advantage despite growth. Japan consistently led with RCA values above 3, peaking at 7.81 in 2017, while South Korea’s RCA improved to around 2.5. ASEAN, Australia, and New Zealand had RCA values below 1, indicating disadvantages in electric car trade.
| Year | China | Japan | South Korea | Australia | New Zealand | ASEAN |
|---|---|---|---|---|---|---|
| 2013 | 0.62 | 1.58 | 0.35 | 0.49 | 0.03 | 0.64 |
| 2014 | 0.40 | 1.56 | 0.32 | 0.37 | 0.01 | 0.49 |
| 2015 | 0.44 | 1.55 | 0.36 | 0.41 | 0.00 | 0.06 |
| 2016 | 0.65 | 1.70 | 0.32 | 1.09 | 0.09 | 0.08 |
| 2017 | 0.06 | 7.81 | 2.63 | 0.01 | 0.04 | 0.13 |
| 2018 | 0.08 | 6.89 | 2.52 | 0.07 | 0.02 | 0.12 |
| 2019 | 0.13 | 6.22 | 2.50 | 0.03 | 0.03 | 0.04 |
| 2020 | 0.18 | 4.70 | 1.98 | 0.01 | 0.01 | 0.09 |
| 2021 | 0.37 | 3.53 | 2.04 | 0.01 | 0.01 | 0.06 |
| 2022 | 0.65 | 3.09 | 2.28 | 0.00 | 0.02 | 0.04 |
The TC index measures net export capability and is calculated as: $$TC_{ij} = \frac{X_{ij} – M_{ij}}{X_{ij} + M_{ij}}$$ where \(X_{ij}\) and \(M_{ij}\) represent exports and imports of electric cars for country i, respectively. Values range from -1 to 1, with positive values indicating export competitiveness. China’s TC for electric cars fluctuated, turning positive in 2021 and reaching 0.52 in 2022, reflecting improved trade balance. Japan maintained high TC values above 0.8, demonstrating strong competitiveness, while South Korea’s TC recovered to positive levels after 2017. Australia and New Zealand had negative TC values, close to -1, highlighting their reliance on imports. ASEAN’s TC varied but generally remained negative, indicating a net import position.
| Year | China | Japan | South Korea | Australia | New Zealand | ASEAN |
|---|---|---|---|---|---|---|
| 2013 | 0.10 | 0.67 | 0.52 | -0.57 | -0.98 | -0.05 |
| 2014 | -0.12 | 0.90 | -0.21 | -0.53 | -0.99 | -0.09 |
| 2015 | -0.05 | 0.68 | -0.14 | -0.72 | -1.00 | -0.82 |
| 2016 | 0.43 | 0.86 | -0.14 | -0.21 | -0.96 | -0.81 |
| 2017 | -0.80 | 0.94 | 0.64 | -0.95 | -0.92 | 0.31 |
| 2018 | -0.71 | 0.95 | 0.61 | -0.76 | -0.97 | -0.17 |
| 2019 | -0.65 | 0.96 | 0.64 | -0.93 | -0.95 | -0.68 |
| 2020 | -0.30 | 0.90 | 0.39 | -0.97 | -0.98 | 0.02 |
| 2021 | 0.17 | 0.84 | 0.34 | -0.98 | -0.99 | -0.22 |
| 2022 | 0.52 | 0.83 | 0.41 | -0.99 | -0.99 | -0.43 |
To synthesize these indices, I apply the entropy method for a comprehensive competitiveness score. This involves normalizing the data, calculating entropy values, and determining weights. The steps are as follows: First, normalize each indicator: $$X’_{ijk} = \frac{X_{ijk}}{X_{\max}}$$ where \(X_{ijk}\) is the value of indicator k for country i in year j, and \(X_{\max}\) is the maximum value. Then, compute the proportion: $$y_{ijk} = \frac{X’_{ijk}}{\sum_i \sum_j X’_{ijk}}$$ Next, calculate the entropy value: $$e_k = -p \cdot \sum_i \sum_j y_{ijk} \ln(y_{ijk})$$ where \(p = \frac{1}{\ln(tm)}\), with t being the number of years and m the number of countries. The differentiation coefficient is: $$g_k = 1 – e_k$$ Finally, the weight for each indicator is: $$W_k = \frac{g_k}{\sum_k g_k}$$ and the comprehensive score is: $$T_{ij} = \sum_k W_k X’_{ijk} \times 100\%$$ For this analysis, the weights for IMS, RCA, and TC are 38.78%, 43.22%, and 18%, respectively. The results show that China’s score fluctuated, dropping to a low of 3.17 in 2017 due to policy adjustments like subsidy reductions, but recovering to 30.08 by 2022. Japan consistently led with scores peaking at 99.82 in 2017, while South Korea maintained steady growth. ASEAN, Australia, and New Zealand had lower scores, indicating weaker competitiveness.
| Year | China | Japan | South Korea | Australia | New Zealand | ASEAN |
|---|---|---|---|---|---|---|
| 2013 | 22.55 | 31.53 | 17.18 | 7.47 | 0.36 | 17.64 |
| 2014 | 16.47 | 33.25 | 10.23 | 6.95 | 0.15 | 15.28 |
| 2015 | 18.79 | 31.35 | 11.34 | 5.43 | 0.01 | 2.50 |
| 2016 | 27.43 | 35.07 | 10.91 | 14.91 | 0.89 | 2.94 |
| 2017 | 3.17 | 99.82 | 40.33 | 0.54 | 0.97 | 13.99 |
| 2018 | 4.37 | 88.94 | 38.61 | 2.71 | 0.39 | 9.41 |
| 2019 | 6.07 | 81.64 | 37.93 | 0.86 | 0.64 | 3.56 |
| 2020 | 10.79 | 64.97 | 30.96 | 0.36 | 0.24 | 10.72 |
| 2021 | 19.74 | 51.55 | 31.03 | 0.25 | 0.15 | 8.08 |
| 2022 | 30.08 | 46.23 | 33.89 | 0.11 | 0.21 | 5.90 |
To identify factors influencing China’s electric car exports to RCEP members, I use a fixed-effects panel data model. The dependent variable is the natural logarithm of China’s electric car export value to RCEP country j in year t, denoted as \(\ln EX_{ijt}\). Independent variables include the GDP of the importing country (\(\ln GDP_{jt}\)), liner shipping connectivity index (\(\ln SHP_{jt}\)), population (\(\ln POP_{jt}\)), China’s R&D expenditure in automobile manufacturing (\(\ln EXP_{it}\)), and China’s GDP (\(\ln GDP_{it}\)). Data sources include UN Comtrade and the World Bank. The model is specified as: $$\ln EX_{ijt} = \beta_0 + \beta_1 \ln GDP_{jt} + \beta_2 \ln SHP_{jt} + \beta_3 \ln POP_{jt} + \beta_4 \ln EXP_{it} + \beta_5 \ln GDP_{it} + \mu_i + \epsilon_{it}$$ where \(\mu_i\) represents country-specific fixed effects, and \(\epsilon_{it}\) is the error term. Correlation analysis shows positive relationships between export value and GDP, shipping connectivity, R&D, and China’s GDP, but further regression is needed for causal insights.
| Variable | lnEXijt | lnGDPjt | lnSHPjt | lnPOPjt | lnEXPit | lnGDPit |
|---|---|---|---|---|---|---|
| lnEXijt | 1 | 0.493*** | 0.429*** | 0.160 | 0.598*** | 0.661*** |
| lnGDPjt | 0.493*** | 1 | 0.652*** | 0.470*** | 0.065 | 0.064 |
| lnSHPjt | 0.429*** | 0.652*** | 1 | 0.131 | 0.107 | 0.105 |
| lnPOPjt | 0.160 | 0.470*** | 0.131 | 1 | 0.020 | 0.020 |
| lnEXPit | 0.598*** | 0.065 | 0.107 | 0.020 | 1 | 0.975*** |
| lnGDPit | 0.661*** | 0.064 | 0.105 | 0.020 | 0.975*** | 1 |
Model specification tests, including F-test and Hausman test, confirm the suitability of the fixed-effects model (p < 0.05). The regression results indicate that the GDP of importing countries has a negative coefficient (-7.546) significant at the 1% level, suggesting that higher economic development in RCEP members may reduce demand for China’s electric car imports due to market saturation or local competition. Shipping connectivity and population show insignificant effects. China’s R&D expenditure negatively impacts exports (-7.310, significant at 1%), possibly due to increased costs or misalignment with market needs. In contrast, China’s GDP has a strong positive effect (24.338, significant at 1%), highlighting the role of economic scale in enhancing electric car competitiveness. For robustness, I replace R&D expenditure with the annual number of automobile patent grants in China (\(\ln PAT_{it}\)), and the results remain consistent, confirming the model’s reliability.
| Variable | Fixed Effects | Random Effects |
|---|---|---|
| lnGDPjt | -7.546*** (2.702) | 0.734** (0.312) |
| lnSHPjt | -1.198 (1.206) | 0.139 (0.487) |
| lnPOPjt | 6.214 (7.166) | -0.114 (0.268) |
| lnEXPit | -7.310*** (2.030) | -9.086*** (2.068) |
| lnGDPit | 24.338*** (3.154) | 22.856*** (3.204) |
| Cons | -536.956*** (113.106) | -588.151*** (73.599) |
| N | 99 | 99 |
| Adj. R² | 0.691 | – |
In conclusion, China’s electric car industry has demonstrated robust growth within the RCEP framework, with expanding export scales and evolving market distributions. Competitiveness analysis reveals that China, along with South Korea and Japan, holds a strong position, while Australia and New Zealand face challenges, and ASEAN shows potential. The empirical analysis underscores that factors such as importing countries’ GDP, China’s R&D investments, and China’s economic scale significantly influence electric car exports. To foster further development, I recommend enhancing product adaptability to meet diverse market needs, such as offering advanced features in developed RCEP markets and cost-effective models in emerging economies. Additionally, diversifying into non-RCEP markets like Europe and Africa can mitigate risks. Optimizing R&D resources by focusing on applied research and cost control, while improving innovation conversion through collaboration and market feedback, is crucial. Strengthening the electric car supply chain via regional integration under RCEP rules, such as leveraging raw material partnerships and establishing assembly hubs, can boost competitiveness. Finally, utilizing RCEP provisions for tariff reductions and trade facilitation will support the high-quality growth of China’s electric car exports, ensuring long-term sustainability in the global EV market.