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# Escalating economic costs of invasive species in China driven by hidden impacts and policy gaps

Peilin Wang, Yantao Xue, Hongbo Jiang, Chunlong Liu, Chi Zhang, Hongkun

Huang, Guifen Zhang, Fanghao Wan, Yibo Zhang, Franck Courchamp

► To cite this version:

Peilin Wang, Yantao Xue, Hongbo Jiang, Chunlong Liu, Chi Zhang, et al.. Escalating economic costs of invasive species in China driven by hidden impacts and policy gaps. Entomologia Generalis, 2025, 45, pp.667 - 678. 10.1127/entomologia/3171. hal-05396973

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Submitted on 4 Dec 2025

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# Escalating economic costs of invasive species in China driven by hidden impacts and policy gaps

Peilin Wang<fnref n="1" />,<fnref n="2" />, Yantao Xue<fnref n="1" />, Hongbo Jiang<fnref n="2" />, Chunlong Liu<fnref n="3" />, Chi Zhang<fnref n="4" />, Hongkun Huang<fnref n="4" />, Guifen Zhang<fnref n="1" />, Fanghao Wan<fnref n="1" />, Yibo Zhang<fnref n="1" />,*, Franck Courchamp<fnref n="5" />

With 7 figures and 2 tables

**Abstract:** Invasive alien species (IAS) lead to substantial economic impacts globally. Accurate estimation of these costs is critical for developing policies to manage IAS effectively, as economic costs reflect the scale of invasion consequences. Despite the expected close linkage between economic costs and management policies of IAS, their interplay remains underexplored. China ranks among the countries most severely impacted by IAS and has implemented numerous management initiatives. Here, we focus on 11 major agriculture and forestry IAS in China to (i) comprehensively estimate their total economic costs and (ii) explore the relationship between these costs and policy development since the 1980s. By scanning 9710 relevant references, we identified 431 cost data, with 87% were new (hidden costs), so missed by the InvaCost database, likely due to their being in the Chinese 'pest'-focused literature. The total economic cost of these 11 IAS amounted to US$ 236.35 billion, with US$ 223.25 billion attributable to hidden cost, revealing severe underestimation of current cost assessments. Economic costs exhibited a strong positive correlation with the number of national IAS management policies, indicating synchronized growth between economic costs and regulatory interventions. Our findings underscore the need to address terminology and language biases in databases like InvaCost to achieve comprehensive IAS cost assessments and guide efficient policy formulation.

**Keywords:** agriculture, forestry, Policy development, InvaCost, pest, invasive alien species

## 1 Introduction

Invasive alien species (IAS) impose massive impacts around the world, including biodiversity loss, ecosystem alterations, and economic costs across multiple sectors (IPBES 2023). Accelerated by globalization vectors and climate-mediated range expansions, biological invasions now outpace natural adaptation mechanisms (Seebens et al. 2020). This paradigm shift has catalyzed global policy reforms, with 68% of UN member states adopting integrated biosecurity frameworks since 2015 to mitigate bioeconomic liabilities through pre-border risk profiling and post-invasion rapid response protocols (Pyšek et al. 2008). Accurate estimation of economic costs is critical for prioritizing and developing strategies to effectively manage invasive species (Diagne et al. 2020a).

However, assessing the actual economic costs of IAS has been a major challenge for ecologists and economists worldwide, particularly through quantitative analyses of costs incurred in key economic activities.

The InvaCost database has revolutionized economic impact assessments of IAS, establishing the first globally standardized framework for synthesizing monetized invasion costs with dynamic updatability and methodological transparency (Diagne et al. 2020a; Diagne et al. 2020b). In recent years, an increasing number of studies have performed the assessment of economic costs of invasive species based on InvaCost database. Currently, published research include (i) 8 regional-scale studies, (ii) 16 country-level analyses, and (iii) 11 taxonomic assessments etc. (Ahmed et al. 2023). Although it provides useful information, InvaCost compiles

<footnote n="1">¹ State Key Laboratory for Biology of Plant Diseases and Insect Pests, Key Laboratory of Invasive Alien Species Control of Ministry of Agriculture and Rural Affairs, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China</footnote>

<footnote n="2">² College of Plant Protection, Southwest University, Chongqing 400715, China</footnote>

<footnote n="3">³ College of Fisheries, Ocean University of China, Qingdao 266100, China</footnote>

<footnote n="4">⁴ Rural Energy and Environment Agency, Ministry of Agriculture and Rural Affairs, Beijing 100125, China</footnote>

<footnote n="5">⁵ Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Société & Evolution, Gif Sur Yvette, France</footnote>

<footnote>* Corresponding author: zhangyibo@caas.cn</footnote>
data from studies with a special focus on invasions, using its
classical terminology, while invasive species are also repre-
sented as other terms when used in other fields (Soto et al.
2024). Additionally, a global overview of invasion biology
has recognized the gaps caused by using only English refer-
ences (Lowry et al. 2012). Although it already includes data
from sources in 25 non-English languages, the persistent
language bias has thus also been recognized as an issue in
the InvaCost database. For example, despite their significant
contributions to understanding biological invasions, data
from Chinese sources make up only 1% of the dataset in
the latest database version due to the database with limited
language coverage. The high proportion of English sources
means that missing non-English materials may strongly limit
the comprehensive assessment and understanding of eco-
nomic costs due to IAS (Angulo et al. 2021).

In addition, in many socio-economic sectors, invasive
species causing impacts are not always termed as ‘invasive
species’. In the context of agriculture and forestry (here-
after referred to agroforestry), many invasive species are
frequently referred as ‘pest’ from a management perspec-
tive, regardless of their geographic origin. Consequently,
references to agroforestry management in databases such as
InvaCost are notably scarce, and associated economic costs
have not been adequately identified or included, thereby hin-
dering a comprehensive overview of the economic costs of
biological invasions (Diagne et al. 2020b). For example, the
Colorado potato beetle (Leptinotarsa decemlineata) invaded
Michigan and caused more than US$ 13.3 million on potato
industry in 1994, yet it was primarily referred as a crop pest
rather than an invasive species (Grafius 1997). As a result,
inconsistent or non-standardized terminology undoubtedly
restricts the compilation of economic cost data for IAS (Soto
et al. 2024). To date, no studies have examined the differ-
ences in economic costs between datasets compiled using
different terms, limiting our understanding invasion impacts
in specific sectors. For instance, a recent study in Brazil
found that the economic costs of invasive species in agricul-
ture were significantly underestimated due to the use of dif-
ferent terms (Faria et al. 2023). Similarly, in Europe, studies
have shown that the economic impacts of invasive species
in forestry are often overlooked when they are categorized
under broader terms like ‘forest pests’ (Turbelin et al. 2023).
Therefore, achieving a more comprehensive understanding
of the economic burden of IAS requires a thorough assess-
ment of the effects of using a combination of different terms.

A comprehensive evaluation of economic cost of IAS
is essential for accurately quantifying the scale and impact
of invasions within a country or region, thereby fostering
enhanced national management and policy management.
Despite existing policies aimed at mitigating the adverse
effects of invasive species, the rate of new invasions contin-
ues to rise unprecedentedly (Pinto et al. 2022), indicating that
policy formulation often lags behind the actual occurrence
of IAS. Furthermore, current global IAS policies emphasize

control over prevention, with 78% focusing on established
invaders versus emerging threats (Faria et al. 2023). This
approach is partly due to the challenges in accurately assess-
ing the extent and impacts of IAS in specific regions.

China has been severely affected by biological invasions
(Zhao 2024), and the cost of invasions to agroforestry has
been particularly high (Paini et al. 2016). According to the
*2020 Bulletin of Ecological-Environmental Situation* issued
by the Ministry of Ecological Environment, the number of
IAS in China has reached 660, over 80% of which adversely
affect agroforestry. The economic costs caused by invasive
insects alone exceeds US$ 7 billion (Wang et al. 2017).
However, these estimates have been critiqued for method-
ological limitations, including extrapolation from unclear
baselines and lack of spatio-temporal granularity (Liu et al.
2021). In response to increasing IAS invasions, the Chinese
government has implemented extensive management poli-
cies at various levels since 1980s (Zhao 2024). What are
the economic costs attributable to IAS in China's agrofor-
stry sector? What is the relationship between economic
costs attributable to IAS and management policies in China?
These questions have rarely been systematically addressed.

In this study, we focused on agroforestry invaders in
China to comprehensively assess their economic costs and
further explore the relationship between these costs and
management policies. First, based on the availability of
economic cost data, we selected 11 representative agrofor-
stry invasive species in China. We collected detailed eco-
nomic costs data and conducted extensive reference searches
in both English and Chinese to compare the costs associ-
ated with these species referred to as invader and as pest.
Additionally, we analyzed the differences in InvaCost and
hidden cost data (i.e., economic costs of IAS not present
in InvaCost, because reported as pests in the literature) for
these 11 species between English and non-English sources.
Second, we evaluated the total reported economic costs of
agroforestry invasive species from both InvaCost database
and hidden cost data related to IAS. Finally, we analyzed
the temporal trends of total costs, InvaCost costs, and hidden
costs, and investigated whether changes in these costs over
time were correlated with China's invasion management
policies. This study aims to provide valuable insights into
the compilation of economic cost data for IAS across dif-
ferent sectors, thereby supporting the development of more
comprehensive management strategies.

## 2 Materials and methods

### 2.1 Species selection from InvaCost, and their costs

Given that the latest version of the InvaCost database (v4.1)
contains 74 data entries across 36 invasive species from
China, we initially reviewed the national list of invasive spe-
cies and gathered data on all 36 species classified as ‘pest’
in agroforestry sectors. However, due to varying degrees of
harm, most species have limited data entries (less than 10)
in China. To accurately assess economic costs, we selected
species with more than 20 data entries and economic impacts
exceeding US$ 100 million for detail analysis. This selection
balances data availability with the significance of economic
impact, ensuring a sufficient sample size while focusing on
species causing substantial economic damage. We identified
11 key agroforestry invasive species based on both economic
costs and data availability: *Bemisia tabaci*, *Hyphantria cunea*,
*Lissorhoptrus oryzophilus*, *Solenopsis invicta*, *Liriomyza
sativae*, *Spodoptera frugiperda*, *Bactrocera dorsalis*, *Cydia
pomonella*, *Leptinotarsa decemlineata*, *Pomacea canaliculata*,
and *Bursaphelenchus mucronatus*. Additionally, since
2023, the Ministry of Agriculture and Rural Affairs (MARA)
of China has designated these 11 species as key species of
concern and implemented targeted quarantine and manage-
ment measures due to their significant threats to China's
agroforestry sectors and resultant substantial economic costs.

## 2.2 Data collection and screening

### 2.2.1 Economic cost data for the 11 species obtained from InvaCost

We performed a more targeted search to compile economic
cost data by searching references specifically for the afore-
mentioned 11 invasive species following the PRISMA
(Preferred Reporting Items for Systematic Reviews and
Meta-Analyses) guidelines (Fig. 1) (Moher et al. 2010).
For each species, we first searched Chinese references in
two comprehensive databases, China National Knowledge
Infrastructure (CNKI; https://www.cnki.net) and Wanfang
data (https://www.wanfangdata.com.cn), as well as English
references in Web of science (https://webofscience.com/)
and Google search engine (https://www.google.com/) in
September 2023 using the search terms: “TS=((Latin name
of each species) AND China AND (econom* OR cost OR
monetary OR dollar OR euro OR ‘sterling pound’ OR RMB)
AND (invasi* OR alien OR non-indigenous OR nonindig-
enous OR nonnative OR exotic OR invader OR naturali* OR
pest))”. Basically, the words “pest” and the currency “RMB”
were added to the original search string used by Diagne et al.
(2020a). Subsequently, we filtered out duplicated and unre-
lated references in R software. Following this, we excluded
irrelevant topics and references that did not focus on eco-
nomic costs after reviewing the titles and abstracts of 9710
related references. Finally, we carefully reviewed all refer-
ences to exclude those that did not report economic costs.
A total of 301 references were retained for subsequent cor-
relation analysis. From each reference, we collected the eco-
nomic cost data for IAS. We organized the collected costs
using the InvaCost template, converting them into a com-
mon currency (2017 US dollars), and subsequently describ-
ing them using various descriptors (all descriptors are mainly

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart detailing the number of references identified from targeted searches. References searches were primarily conducted in the Chinese databases CNKI and Wanfangdata, as well as the English databases Web of Science and Google search engine.</figcaption></figure>

based on the different descriptive ‘columns’ in the InvaCost
database https://doi.org/10.6084/m9 figshare.12668570.v5).

For costs with a time span, we calculated the median and
documented the minimum and maximum values mentioned
in the reference by using ‘expandYearlyCosts’ function in
‘invacost’ R package. Additionally, we characterized the
temporal status and range of costs based on the ‘Period_of
estimation’, ‘Time_range’, and ‘Occurrence’ columns. If the
specific year in which the cost was incurred was not men-
tioned in the reference, we adopted a conservative approach
by using the publication year of reference as the proxy year.
We further classified the types of cost data according to the
‘Impacted sector’ and ‘Type of cost merged’ columns
and recorded the geographic regions where costs occurred
(‘Province’ column). Regarding the assessment of data reli-
ability in the ‘Method reliability’ columns, since the raw data
were extracted from published references, we deemed them
to be ‘highly reliable (peer-reviewed reference)’. To ensure
the validity of the data, each set of cost data and its source
references were reviewed by two authors, and the data after
checking is retained for subsequent analysis. Furthermore,
we extracted data from the latest version of the InvaCost
database (v4.1) on the economic costs caused by the selected
11 species and invasive species in agriculture and forestry to
China for comparative analysis (Diagne et al. 2020a).

### 2.2.2 National policy data

To gain a deeper understanding China’s strategies for the
prevention and control of IAS, we conducted a systematic
review of state-implemented policy measures since 1980.
Policies were categorized into two tiers: national-level poli-
cies issued by the central government and province-level policies issued by local governments. National-level data were sourced from official website of key government bodies, including the Ministry of Agriculture and Rural Affairs of the People's Republic of China (https://www.moa.gov.cn/), the National Forestry and Grassland Administration (https://www.forestry.gov.cn/), the General Administration of Customs of the People's Republic of China (http://www.customs.gov.cn/), and the Ministry of Ecology and Environment of the People's Republic of China (https://www.mee.gov.cn/). Provincial-level data were obtained from the official websites of the Agriculture Departments and Forestry and Grassland Administration of the provinces, autonomous regions and municipalities directly under the Central Government. After compilation, the policy data underwent rigorous verification to remove duplicates. Finally, the policies were classified into four categories for further analysis: regulation, prevention measure, science popularization and standard procedure. Regulations establish a legal framework (Liu et al. 2023), preventive measures mitigate invasion risks, public education initiatives promote public engagement, and standardized procedures ensure the scientific and effective implementation of control measures. These policy types are interdependent and collectively form a multi-layered, comprehensive management system for IAS.

## 2.3 Data analysis

We conducted our analyses utilizing data from the ‘InvaCost costs’ (i.e., data compiled in the InvaCost database) and ‘hidden costs’ (i.e., data retrieved from studies that classified IAS species as pest). The integration of “InvaCost costs” and “hidden costs” yielded the “total costs”. First, we compared the number of cost entries (no. of costs) and

economic costs between InvaCost and hidden costs for 11 species. Additionally, we briefly analyzed the differences in data sources for InvaCost and hidden costs between English and non-English reference for 11 species. Secondly, we compared the economic costs of 36 agroforestry invasive species in China recorded in InvaCost with those of the 11 specifically retrieved species, evaluating the contribution of hidden costs and Invacost costs. Furthermore, we assessed the variations in total economic costs across geographic regions and impact sectors (specifically agriculture and forestry). Each data entry was assigned to the corresponding province and one of seven larger regions based on China’s administrative divisions (Wu & Wang 2013). Concurrently, we analyzed the temporal trends of invasion, hidden, and total costs from 1980 to 2023. Then, we also evaluated the temporal trends of various types of cost and differences in economic costs. Finally, we examined the temporal trend of cumulative polices over time and conducted a correlation analysis and linear fitting between the economic costs and the number of policies. All analyses were performed in R software (v 4.3.2) by using package ‘invacost’, ‘ggplot’, ‘tidverse’ etc.

# 3 Results

## 3.1 Economic costs of agroforestry invasive species

For the 11 representative invasive species, there is a significant discrepancy between the economic costs reported in InvaCost and those identified as hidden cost. Our datasets comprise 378 hidden cost records, all sourced from non-English sources (Chinese), resulting in a total hidden cost of US$ 223.25 billion. In contrast, the InvaCost database

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** InvaCost cost and hidden cost of 11 agroforestry invasive species were compared in (a) cost records and total economic costs; and (b) economic costs of each species.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Pie chart and stacked histogram shows the number of cost entries and economic costs composition for hidden cost and InvaCost cost. Organism pictograms of the top four species with the highest economic costs of the two cost are shown in the stacked histogram.</figcaption></figure>

includes 74 records with a total cost of US$ 10.33 billion in China, comprising US$ 5.03 billion from English subset and US$ 5.3 billion from the non-English subset (Fig. 2a, **Supplementary Table S1**). For agroforestry, the total economic costs attributed to IAS amount to US$ 236.35 billion. The dataset encompasses a total of 431 economic cost data, of which 87% (378 entries) represent new-found cost data (hidden costs), while only 13% (53 entries) are previous-found data (InvaCost cost) (Fig. 3). Notably, the species contributing more significantly to hidden costs are *Spodoptera frugiperda* (US$ 122.83 billion, with no economic cost data available in InvaCost) and *Bursaphelenchus mucronatus* (US$ 33.16 billion compared to US$ 5.94 billion in InvaCost) (Fig. 2b).

## 3.2 Geographical compositions and impacted sector of economic costs

There are significant geographical variations in economic costs in China (Fig. 4). Southern China experienced the highest total costs, reaching up to US$ 5.7 billion. The three provinces with the highest economic costs were Guangdong (US$ 5.47 billion), Liaoning (US$ 4.26 billion) and Shaanxi (US$ 3.31 billion) (Table 1). Agroforestry sector accounted for 82% (US$ 203.7 billion) and 18% (US$ 43.9 billion) of the total economic costs, respectively (Fig. 4). The highest agricultural costs in Southern China amounted to US$ 5.69 billion, originating exclusively from Guangdong and Guangxi provinces. The highest forestry costs were observed in Northwestern China (US$ 3.28 billion), which were only reported from Shaanxi Province (Table 1).

## 3.3 Temporal trend of economic costs

The average annual costs from 1980 to 2023 amounted to US$ 5.31 billion, with US$ 0.23 billion attributed to InvaCost

and US$ 5.07 billion categorized as hidden costs (Fig. 5). There were notable variations in these costs across different years. Hidden costs exhibited fluctuations over the period but demonstrated a consistent upward trend, increasing from US$ 279,000 in 1980 to US$ 43.19 billion in 2023. The peak hidden cost was recorded in 2020 at US$ 72.52 billion. A significant growth phase for hidden cost began in 2014, with an average annual increase of approximately US$ 4.79 billion (Fig. 5). In contrast, InvaCost cost remained relatively stable between 1980 and 1998, averaging US$ 0.15 billion annually. It reached its peak in 1999 at US$ 2.15 billion and subsequently fluctuated between US$ 0.64 and US$ 1.24 billion from 2000 to 2019 (Fig. 5). Importantly, no cost data is available in the InvaCost database for the years 2020 to 2023, which may suggest potential lagged costs that are substantial.

## 3.4 Types of cost

There were significant variations in the proportions of different types of economic costs (Fig. 6). The total damage and management costs amounted to US$ 150.86 billion and US$ 4.54 billion, respectively. The average annual costs were US$ 3.42 billion and US $1.03 billion, respectively. Both categories of costs exhibited fluctuations but demonstrated a consistent upward trend over time. Notably, the damage cost was particularly pronounced, increasing from US$ 0.15 billion in 1980 to US$ 33.36 billion in 2023 (Fig. 6a). Additionally, both damage and management costs showed rapidly increases starting from 2014, with management costs rising sharply by US$ 1.22 billion from 2014 to 2017. Furthermore, damage costs accounted for 64.5% of the total, with hidden cost comprising 95% of this category. In contrast, management costs constituted only 2% of the total, with hidden costs making up 90% of this category (Fig. 6b).

## 3.5 Temporal trend of polices and correlation analysis with economic costs

Since 1980, the Chinese governments have issued a total of 352 policies on IAS at both national (N = 270) and provincial (N = 82) levels. National-level policies primarily focus on standardized quarantine procedures for various invasive species, while provincial-level policies predominantly emphasize science popularization, with 80% dedicated to this purpose (Table 2). Prior to 2006, the number of policies remained relatively stable, but subsequently exhibited a significant upward trend over time (Fig. 7a). This increase correlates strongly with rising economic costs, as evidenced by linear fitting and correlation analysis (Pearson correlation = 0.629, Pearson correlation coefficient (r) = 0.629, 95% CI [0.52, 0.73], *P* < 0.01, Fig. 7b). The observed trends suggest that heightened public and government awareness may have driven more proactive policy formulation and more thorough reporting of economic cost.
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Distribution of economic costs of 11 species in different regions and impact sectors of China. Pie charts showing the share of economic costs in the agriculture and forestry sectors in each region and combining organism pictograms of the species with the highest costs in the different sectors.</figcaption></figure>

<table><caption>**Table 1.** Distribution of economic costs (US$ Million) across provinces in China with different impact sectors. Gray shaded areas are the highest cost provinces in each region. The thickened provinces are the top three provinces with the highest economic costs.</caption><thead><tr><th>Region</th><th>Province</th><th>Agriculture cost</th><th>Forestry cost</th><th>Total cost</th></tr></thead><tbody><tr><td rowspan="4">Southern China</td><td>Guangdong</td><td>5447.3</td><td>17.4</td><td>5464.7</td></tr><tr><td>Guangxi</td><td>192.6</td><td>0.3</td><td>192.9</td></tr><tr><td>Hainan</td><td>52.0</td><td>0</td><td>52.0</td></tr><tr><td>Hong Kong</td><td>0</td><td>0</td><td>0</td></tr><tr><td></td><td>Macao</td><td>0</td><td>0</td><td>0</td></tr><tr><td colspan="2">Total cost (US$ Million)</td><td>5691.9</td><td>17.7</td><td>5709.6</td></tr><tr><td rowspan="10">Eastern and Central China</td><td>Shanghai</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Jiangsu</td><td>21.3</td><td>1.3</td><td>22.6</td></tr><tr><td>Zhejiang</td><td>359.7</td><td>578.8</td><td>936.5</td></tr><tr><td>Anhui</td><td>8.7</td><td>29.0</td><td>299.2</td></tr><tr><td>Jiangxi</td><td>0.067</td><td>122.3</td><td>122.4</td></tr><tr><td>Fujian</td><td>249.7</td><td>16.7</td><td>417.6</td></tr><tr><td>Shandong</td><td>456.4</td><td>663.9</td><td>1120.3</td></tr><tr><td>Henan</td><td>0.3</td><td>0</td><td>0.3</td></tr><tr><td>Hubei</td><td>1861</td><td>33.3</td><td>1894.3</td></tr><tr><td>Hunan</td><td>248.4</td><td>19.7</td><td>268.1</td></tr><tr><td colspan="2">Total cost (US$ Million)</td><td>3205.5</td><td>1465.0</td><td>4670.5</td></tr></tbody></table>
<table><caption>**Table 1.** continued.</caption><thead><tr><th>Region</th><th>Province</th><th>Agriculture cost</th><th>Forestry cost</th><th>Total cost</th></tr></thead><tbody><tr><td rowspan="3">Northeastern China</td><td>Heilongjiang</td><td>277.2</td><td>32.8</td><td>310</td></tr><tr><td>Jilin</td><td>41.7</td><td>0</td><td>41.7</td></tr><tr><td>Liaoning</td><td>4012.2</td><td>248.3</td><td>4260.5</td></tr><tr><td colspan="2">Total cost (US$ Million)</td><td>4331.1</td><td>281.1</td><td>4612.2</td></tr><tr><td rowspan="5">Northwestern China</td><td>Shannxi</td><td>18.8</td><td>3288.5</td><td>3307.3</td></tr><tr><td>Gansu</td><td>39.4</td><td>0</td><td>39.4</td></tr><tr><td>Qinghai</td><td>15.8</td><td>0</td><td>15.8</td></tr><tr><td>Ningxia</td><td>3.4</td><td>0</td><td>3.4</td></tr><tr><td>Xinjiang</td><td>642.2</td><td>0</td><td>642.2</td></tr><tr><td colspan="2">Total cost (US$ Million)</td><td>719.6</td><td>3288.5</td><td>4008.1</td></tr><tr><td rowspan="5">Southwestern China</td><td>Sichuan</td><td>1839.3</td><td>0.4</td><td>1839.7</td></tr><tr><td>Chongqing</td><td>60.3</td><td>28.9</td><td>89.2</td></tr><tr><td>Yunan</td><td>260.2</td><td>17.2</td><td>277.4</td></tr><tr><td>Guizhou</td><td>3.7</td><td>5.7</td><td>9.4</td></tr><tr><td>Xizang</td><td>0</td><td>0</td><td>0</td></tr><tr><td colspan="2">Total cost (US$ Million)</td><td>2163.5</td><td>52.2</td><td>2215.7</td></tr><tr><td rowspan="5">Northern China</td><td>Beijing</td><td>2.7</td><td>50.7</td><td>53.4</td></tr><tr><td>Tianjin</td><td>0</td><td>1.2</td><td>1.2</td></tr><tr><td>Hebei</td><td>146.9</td><td>0.5</td><td>147.4</td></tr><tr><td>Inner Mongolia</td><td>0</td><td>116.2</td><td>116.2</td></tr><tr><td>Shanxi</td><td>10.8</td><td>1991.2</td><td>2002.0</td></tr><tr><td colspan="2">Total cost (US$ Million)</td><td>160.4</td><td>2159.8</td><td>2320.2</td></tr><tr><td colspan="2">Taiwan</td><td>530.5</td><td>0</td><td>530.5</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** The temporal trend in the three types of economic costs from 1980 to 2023.</figcaption></figure>

## 4 Discussion

Using invasive species in agroforestry as a case study, we conducted a systematic investigation into the economic costs of IAS in China. Our findings reveal a significant discrepancy between the economic costs associated with species classified as invaders in the scientific references and those categorized as pests, despite being the same species. For just 11 representative invasive species, the total economic costs when referred to as “pest” amounted to US$ 223.25 billion, an order of magnitude higher than the costs reported for all China’s agroforestry invasive species in the InvaCost database. Additionally, our study highlights the role of national policies in facilitating more accurate assessment of invasion costs by analyzing temporal trends. Both management costs and total costs exhibited a notable increase following the implementation of national regulations in China.

The hidden costs of 11 invasive agroforestry species constitute up to 95% of the total economic costs of invasive species in China’s agroforestry sector. This underscores the necessity for future studies to more comprehensively com-
<figure><img src="image_8.png" /><figcaption>Fig. 6. Damage and management (a) time trends from 1980 to 2023 and (b) the proportions in total economic costs. Data from hidden costs were shown in a darker shade and from InvaCost costs were in a lighter shade.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 7. (a) The temporal trend in cumulative polices from 2002 to 2023. (b) Linear fit and Person correlation of economic costs and national policies.</figcaption></figure>

<table><caption>**Table 2.** The number of different policy types (Nb) and percentage contribution between national (N) and provincial (P) level. Reg.: Regulation, Prev.: Prevention, SciPop.: Science popularization, Sta.: Standard procedure.</caption><thead><tr><th></th><th>Reg.</th><th>Prev.</th><th>SciPop.</th><th>Sta.</th><th>Tot.</th></tr></thead><tbody><tr><td></td><td>Nb (%)</td><td>Nb (%)</td><td>Nb (%)</td><td>Nb (%)</td><td></td></tr><tr><td>N</td><td>17 (6.3)</td><td>66 (24.4)</td><td>68 (25.2)</td><td>119 (41.1)</td><td>270</td></tr><tr><td>P</td><td>0 (0)</td><td>16 (19.5)</td><td>66 (80.4)</td><td>0 (0)</td><td>82</td></tr></tbody></table>

pile data on economic impacts of IAS using a broader range of terminology. This is especially pertinent in the agroforestry sector, where IAS are often categorized as pests rather than invaders. A recent review by Soto et al. (2024) identified ‘pest’ as one of the most frequently used terms to describe alien species, with 2702 papers specifically employing this

term. For example, Jetter & Godfrey (2009) focused exclusively on the control of the *Diaprepes root weevil*, an invasive pest affecting citrus and avocado crops, emphasizing its economic impact on California without addressing its invasiveness when formulating quarantine measures. Meanwhile, Castro et al. (2023) highlighted that terminological ambiguity within invasion science impedes effective reporting of invasive species in regional inventories. The variability in terminology among different fields suggests that studies assessing the economic costs of invasions should encompass terms used in various disciplines. Therefore, accounting for the diverse and flexible terminology of invasive species across sectors is critical for accurately assessing their economic costs. Future efforts should prioritize strengthening collaboration between invasion science and other disciplines to promote the standardization of terminology.
Furthermore, non-English-language data are essential for a comprehensive assessment of the economic costs associated with invasive species. Language bias can significantly influence the assessment of these costs (Lowry et al. 2012). Our findings corroborate this conclusion, as all identified hidden costs originate from non-English datasets. This outcome may be attributed to the fact that economic cost assessments are frequently compiled in national reports aimed at attracting the attention of governmental officials rather than being published in scientific journals. Additionally, local government officials often focus on and gather data regarding the economic impacts of invasive species; however, this information is typically provided by non-researchers and thus predominantly documented in the official language of the country (Nuñez et al. 2019). Therefore, we suggest that future development of the InvaCost database prioritize the integration of multilingual data sources, encourage researchers to thoroughly explore non-English reference, and provide tools for translation and standardization. Such an approach will profoundly enhance our comprehensive understanding and effective management of biological invasions.

Agroforestry sectors globally are significantly impacted by invasive species. The substantial economic burden by IAS on agriculture in China aligns with previous research findings (Paini et al. 2016). According to Turbelin et al. (2023), the aggregated costs from 46 countries, as documented in the InvaCost database, reached US$ 509 billion specifically within the agricultural sector. In the United States, biological invasions have incurred economic costs exceeding US$ 551 billion in the agroforestry sector, with agriculture bearing the largest portion of these costs (Fantle-Lepczyk et al. 2022). In Germany, forestry ranks second in terms of sectors affected by IAS, with associated costs amounting to US$ 1.36 billion, while agriculture incurs costs of US$ 0.58 billion (Haubrock et al. 2021). In Russia, the agroforestry sector is among the top two most affected by biological invasions, with a total cost reaching US$ 565 million (Kirichenko et al. 2021). Similarly, both the United Kingdom and Australia report that agriculture bears the highest economic burden due to IAS. The significant economic impact on agriculture can be attributed to factors such as the continuous surveillance and agriculture's substantial contribution to the global economy, accounting for approximately 4% of global GDP (FAO 2022).

Economic costs have escalated markedly since 2000, probably contributing to the increasing implementation of national regulations in China. This trend is evidenced by the positive relationship between rising economic costs and the formulation of national policies. Since 2004, China has initiated a comprehensive national development strategy framework for IAS and has subsequently introduced a range of policies aimed at prevent and control. As a result, there has been heightened attention and enhanced awareness regarding IAS management (Liu et al. 2023). Meanwhile, both the number of projects and funding allocations have steadily

increased. By 2015, China had invested over US$ 30 million in basic research projects focused on IAS (Wang et al. 2017). Moreover, both total economic costs and management costs have exhibited a rapid upward trend since 2014 (Liu et al. 2023). For instance, according to national reports, China adopted a holistic approach to national security in 2014, integrating biosecurity into its national security framework (Liu et al. 2023). Additionally, the Biosafety Law of the People's Republic of China came into effect on April 15, 2021. On June 17, 2022, four Chinese ministries jointly issued the Regulation on Management of Invasive Alien Species. In summary, China has implemented proactive national policy measures to manage and control IAS. These policies have not only facilitated research on economic cost assessment and resource allocation but also laid a solid foundation for more effective and sustainable IAS management.

Currently, the management of IAS predominantly operates in a reactive mode, with policy implementation typically occurring after damage has already occurred (Kleespies et al. 2024). This implementation gap stems from the hidden economic and ecological costs associated with IAS, which complicates accurate impact quantification. To address this issue, future policymakers should enhance monitoring systems to enable early warning and rapid response (Seebens et al. 2020). Concurrently, conducting thorough economic cost assessments can improve risk analysis by highlighting the cost-effectiveness of different management strategies (Abram et al. 2024). Empirical studies have shown that biological control yield exceptional returns on investment, with benefit-cost ratios ranging from 5:1 to 1000:1 (Naranjo et al. 2015). However, the risks associated with biological control are often overstated, leading to a preference for short-term measures such as pesticide use, which pose broader ecological and health risks (Sargent et al. 2023). Comprehensive economic cost assessments allow decision-makers to better balance long-term benefits and risks, avoiding missed opportunities due to excessive risk aversion. Additionally, strengthening international policy exchange and cooperation, along with establishing an international IAS management framework, will contribute to more effective formulation and implementation of control policies (IPBES 2023). Our study recommends that relevant authorities revise their approach to IAS management, shifting from a reactive to a proactive stance. It emphasizes the importance of strengthening predictive early warning systems and analysis to guide targeted control efforts and enhance the effectiveness of prevention and management measures.

In this study, we observed that the temporal trends of the total economic costs simulated by various models for 11 agroforestry invasive species in China consistently exhibited a continuous upward trajectory. Previous research has indicated that global invasions are expected to increase over time, with a significant increase in the coming decades (Seebens et al. 2020). Our findings corroborate this view, as the future trajectory of total economic costs recorded in the projections
continues to increase over time and shows no signs of slowing down. From 1980 to 2023, a total of 234 invasive species were first reported in China's agroforestry sector, with the cumulative number of IAS increasing exponentially (Wang et al. 2017) (Supplementary Fig. S1), further substantiating our results.

In addition, to ascertain the total number of species, occurrence distribution and damage loss of IAS in China, the Ministry of Agriculture and Rural Affairs (MARA), in collaboration with six other ministries (Ministry of Finance, Ministry of Natural Resources, Ministry of Ecology and Environment, Ministry of Housing and Urban-Rural Development, General Administration of Customs, National Forestry and Grassland Administration), initiated the first nationwide general survey of IAS in 2021, which is led by MARA (Zhao 2024). Furthermore, the prevention and control of IAS was highlighted as a key priority in the report of the 20th National Congress of the Communist Party of China in 2022. However, although China has strengthened biosafety management to address IAS, the rapid expansion of international trade and globalization continues to pose significant challenges for accurate and effective prevention and control. Although the national survey on IAS has been successfully completed, the official survey report has not yet been publicly released. Once available, it will substantially bolster China's capabilities in preventing and controlling IAS.

Furthermore, our study identified substantial discrepancies in direct damage and management costs associated with IAS. A portion of these discrepancies may stem from underreporting of management expenditures, especially those related to reactive measures such as pesticide applications. For instance, following the invasion of fall armyworm (Spodoptera frugiperda) in China, pesticide costs tripled within two years; however, such data are often inconsistently documented (Yang et al. 2021). To improve cost tracking accuracy, it is imperative to establish standardized reporting frameworks that integrate data from agricultural services, retailers, and government subsidies. Besides, certain IAS can exert a broad spectrum of non-economic impacts by adversely affecting human health and the environment, either directly or indirectly, such as through non-target effects of pesticide sprays on beneficial insects (e.g., natural enemies and pollinators) (USGS National Wildlife Health Center 2022). Notably, many of these non-economic impacts cannot be quantified monetarily and are likely underreported or overlooked (IPBES 2023). Therefore, there is an urgent need to advocate for a comprehensive or multidimensional cost accounting framework of IAS.

The collaborative construction of InvaCost offers significant opportunities to estimate the economic costs associated with IAS across various taxonomic groups and geographical regions. These estimates serve as a foundation for assessing standardized monetary impacts of IAS. However, the compilation of economic cost data is an ongoing process

that necessitates systematic strategies for data collection. Our research confirmed that relying solely on invasion science-specific terminology significantly underestimates the economic costs of invasive species due to terminology and language biases present in other disciplines. Furthermore, this study examines the temporal dynamics of economic costs in relation to national policies and management measures aimed at addressing biological invasions, underscoring the critical importance of national attention and initiatives to better control and prevent invasive species. To strengthen global governance of IAS, future priorities should encompass: (1) standardizing terminology within the InvaCost database to eliminate reporting biases; (2) implementing multilingual data validation protocols; and (3) developing predictive cost-modeling frameworks. These improvements will facilitate more rigorous cost-benefit analyses, thereby optimizing policy development and targeted resource allocation in biosecurity operations.

**Acknowledgements:** This research was supported by the National Key Research and Development Program of China (2021YFD1400200, 2023YFC2605200), the French National Research Agency (ANR-14-CE020021), and the BNP-Paribas Foundation Climate Initiative for funding the InvaCost project that allowed the construction of the InvaCost database.

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Manuscript received: November 20, 2024

Revisions requested: February 2, 2025

Revised version received: April 18, 2025

Manuscript accepted: April 30, 2025

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