Research on spatiotemporal evolution and trend prediction of marine economic efficiency in China’s coastal regions – a dynamic analysis using super-SBM and grey forecasting model Open Access

With the vigorous development of the global marine economy, China’s coastal regions serve as the frontline of marine economic development. The evolutionary patterns and future trends in their economic efficiency hold significant implications for national marine strategic planning (Zhou et al., 2023). Against the dual backdrop of rapid global marine economic growth and China’s advancing “Marine Power” strategy, the marine economy has become a core engine driving high-quality development in coastal areas (Zhao et al., 2021). According to the 2024 China Marine Economy Statistical Bulletin, the national gross marine product reached 10.5438 trillion yuan in 2024, marking a 5.9% year-on-year increase and accounting for 7.8% of GDP. However, the sector also faces challenges such as tightening resource-environment constraints and insufficient transformation of scientific innovation (Shao et al., 2021). Li et al. (2021) further reveal that while China’s marine economy showed an upward trend in high-quality development from 2017 to 2019, the overall level remained low, with provinces like Guangdong and Shanghai excelling in scientific and technological drive, but regions such as Guangxi lagging in green sustainability. Scientifically evaluating the development quality of the marine economy and uncovering the spatiotemporal evolution of its efficiency are of practical importance for optimizing marine resource allocation and achieving coordinated regional development.

Research on marine economic efficiency has grown increasingly diverse, with definitions shifting from single-factor to total-factor economic efficiency. Early studies predominantly focused on single sectors or industries. For instance, Tingley et al. (2005), Wanke (2013), and Pham et al. (2014) examined production efficiency in marine fisheries, while Tongzon (2001) and Cullinane et al. (2006) analyzed transport efficiency in port logistics. Current efficiency measurement methods primarily rely on Data Envelopment Analysis (DEA), including models such as Charnes-Cooper-Rhodes (CCR), Banker-Charnes-Cooper (BCC), and Slacks-Based Measure (SBM). (Tone, 2001), and super-SBM (Du et al., 2010; Fang et al., 2013; Tone, 2002). Xu and Gao (2022) applied the DEA to measure green total factor productivity and found that green finance can significantly promote marine economic quality. Compared to classical CCR and BCC models, SBM effectively addresses slack variable issues in efficiency measurement, while super-efficiency SBM further enables comparisons among decision-making units with efficiency scores of 1, making its application in efficiency computation increasingly prevalent.

Existing studies provide crucial support for deepening marine economic efficiency research. In the realm of forecasting and modeling, Li et al. (2019) summarized grey forecasting and relational models and their applications in marine economics and management, offering methodological references for subsequent research. Yin et al. (2021) analyzed and forecasted China’s marine economy development, aiding in understanding its overall trend. Zeng et al. (2024) advanced this by proposing a multivariate grey model for shipping carbon emissions, yet a gap remains in linking efficiency evolution with sustainable development goals.

However, current research primarily focuses on efficiency measurement and influencing factors, paying insufficient attention to systematic investigations of spatiotemporal evolution patterns and dynamic trends. A comprehensive framework integrating historical trend analysis with future scenario prediction remains lacking. In fact, improvements in marine economic efficiency not only manifest as regional disparities in cross-sectional data but also reflect the temporal evolution of total factor productivity (Fang et al., 2024; Zhang et al., 2024). To bridge this gap, this study employs an undesirable-output super-efficiency SBM model, treating carbon emissions as the undesirable output, labor, capital, and technology as inputs, and gross marine product as the desirable output to calculate the marine economic efficiency of China’s 11 coastal provinces. Subsequently, a grey prediction model is constructed to forecast future trends in efficiency dynamics.

The paper is structured as follows: Section 2 introduces the methodology and indicator system; Section 3 presents efficiency calculation results and forecasts future trends in marine economic efficiency; and Section 4 summarizes key findings and proposes policy recommendations.

This section presents the primary analytical methodologies employed in this study, including the Super-efficiency SBM model, grey prediction model, and parameter optimization techniques. Subsequently, it introduced the indicator system for measuring marine economic efficiency.

Traditional radial DEA models measure inefficiency only by including the proportionate reduction (increase) of all inputs (outputs), while ignoring the efficiency values of slack improvements. To address this issue, Tone (2001) proposed the SBM model. However, the SBM model cannot compare the performance of multiple decision-making units (DMUs) with an efficiency value of 1. Therefore, Tone (2002) innovatively introduced a super-efficiency SBM model with undesirable outputs, which enables effective comparison of subtle differences among high-efficiency DMUs and overcomes the slack problem in input-output analysis. Assume a production system with n decision-making units, m input factors, $s1$ desirable outputs, and $s2$ undesirable outputs. Let the vectors be $x∈Rm$⁠, $yd∈Rs1$⁠, $yu∈Rs2$⁠, and $X$⁠, $Yd$⁠, be matrices, where $X=[x1⋯xn]∈Rm×n$⁠, $Yd=[y1d⋯ynd]∈Rs1×n$⁠, $Yu=[y1u⋯ynu]∈Rs2×n$⁠. The constructed super-efficiency SBM model with undesirable outputs is as follows:

In Eq. (1), $φkt$ is the marine economic efficiency value of the k-th DMU in the t-th period. When $φkt<1$⁠, it indicates that the decision-making unit is in an inefficient state; when $φkt≥1$⁠, it indicates that the decision-making unit is efficient, and the larger the $φkt$ value, the higher the efficiency. $xik$⁠, $ypkd$⁠, $yqku$⁠, $λj$ respectively represent the i-th input, the p-th desirable output, the q-th undesirable output, and the j-th linear combination coefficient of the k-th DMU.

The grey forecasting model (GM(1,1)) is typically used for modeling and forecasting data with exponential growth patterns. Its core steps include data accumulation, equalization, the establishment of a differential equation, parameter estimation, and model testing. This model utilizes the 1-AGO for modeling and forecasting. It is important to note that the GM(1,1) model is primarily suitable for data with exponential growth patterns and may not perform well for data with non-exponential growth. Additionally, it relies on the assumption of linear relationships and may not fit well for data with nonlinear relationships. In the process of model selection, it is important to evaluate and validate the suitability of the model based on the specific problem at hand. The fractional grey forecasting model (FGM (1,1)) is a commonly used grey forecasting model (Li et al., 2023; Madhi and Mohamed, 2017; Qiao et al., 2022). When r = 1, the FGM(1,1) model is the grey GM(1,1) model. The FGM(1,1) is established as follows.

Assuming the original sequence is given as $X(0)={x(0)(1),x(0)(2),⋯,x(0)(n)}$⁠. The r-order fractional accumulation generation operator (FAGO) sequence of $X(r)={x(r)(1),x(r)(2),⋯,x(r)(n)}$ is obtained by

where $Cr−10$ = 1, $Ckk+1=0$⁠, $Ck−i+r−1k−i=(k−i+r−1)(k−i+r−2)⋯(r+1)r(k−i)!,k=1,2,⋯,n.$

According to Eq. (2), the coefficient matrix under FAGO is

The whitening differential equation of FGM(1,1) is represented as

where a is called the developing coefficient and b is called the endogenous control grey number.

Solve the parameters by the least squares method:

where

The initial value is $xˆ(r)(1)=x(0)(1)$⁠, the time corresponding sequence is

where $xˆ(r)(k+1)$ is the value at k+1.

For the sequence $Xˆ(r)={xˆ(r)(1),xˆ(r)(2),⋯,xˆ(r)(n)}$ , the reduced sequence is $Xˆ(0)={xˆ(0)(1),xˆ(0)(2),⋯,xˆ(0)(n)}$⁠, where

Through the reduction operation, the predicted number column $Xˆ(0)$ is obtained.

Evaluate the model using mean absolute percentage error (MAPE), where.

In this paper, MAPE is used to evaluate the fitting accuracy of the model. The model shows better results when the MAPE has the smallest values. Generally, when the MAPE value is less than or equal to 10% (Lewis, 1982), the prediction accuracy of the model can be considered excellent. The Lewis criteria for the MAPE value are given in Table 1.

The selection of the accumulation order of FGM(1,1) has a decisive impact on the prediction results, and this paper obtains the optimal order through the particle swarm optimization (PSO) algorithm.

Originating from the simulation of bird flocking behavior, the particle swarm optimization algorithm was proposed by Kennedy and Eberhart (1995). This algorithm has attracted the attention of many scholars due to its advantages of simple implementation, high precision, and fast convergence speed, and has been applied in multiple engineering fields. The pseudo code of PSO is shown in Table 2.

From the above pseudocode, it is evident that when the PSO algorithm satisfies the termination criteria, the optimal accumulation order can be outputted. The flow chart of FGM(1,1) is shown as Figure 1.

Based on the availability of marine economic data from 11 coastal provinces and cities and the empirical accumulation of existing research, this study draws on the research of Teixeira and Queirós (2016) and Guo et al. (2022) to consider the inputs of marine economy from the perspectives of labor, science and technology, and capital. The number of maritime employees in coastal areas is selected as the labor resource input, the number of marine researchers and scientific research funding as the technological input, and the fixed capital stock as the capital input. The gross marine product of coastal areas is selected as the marine economic output indicator, and carbon emissions are selected as the undesirable output indicator. The indicator descriptions are shown in Table 3.

Table 4 summarizes the maximum, minimum, average, and standard deviation of the marine economic input and output indicators for 11 coastal provinces in China from 2011 to 2022. The results show significant gaps between the minimum and maximum values of each statistical indicator, with generally high dispersion. A positive correlation exists between inputs and outputs, where higher inputs lead to higher outputs, satisfying the isotropic assumption between input and output items in the SBM model.

All data in this study were sourced from the China Marine Statistical Yearbook (2011–2022), China Marine Economy Statistical Yearbook (2011–2022), China Statistical Yearbook (2011–2022), and China Energy Statistical Yearbook (2011–2022), with missing values addressed using linear interpolation. This method is suitable for time series data, assuming the change between two adjacent known data points is linear, thereby estimating the value for the missing year. The specific calculation formula is as follows:

Where

To account for the impact of historical fixed-asset investment on marine economic development, the perpetual inventory method was employed to estimate fixed capital stock, calculated as:

where $Kit$ denotes the capital stock of region i in year t, $Kit−1$ represents the capital stock of region i in year t-1, $δit$ is the depreciation rate (set at 10.96%, following Shan (2008)), and $Iit$ is the total fixed capital formation in region i during year t. The base-year capital stock (2010) was derived by dividing the total fixed capital formation in 2011 by the sum of the average depreciation rate (10.96%) and the mean investment growth rate from 2011 to 2015. The marine economy’s fixed capital stock was then obtained by multiplying the provincial capital stock by the proportion of marine industry output in regional GDP.

Data adjustments for fixed capital formation: Since the China Price Statistical Yearbook 2021 discontinued the release of fixed-asset investment price indices from 2021 onward, data for 2020–2022 were estimated using the 2015–2019 provincial average growth rates.

Data adjustments for maritime employment: Since the China Marine Statistical Yearbook has stopped publishing statistics on the number of maritime employees after 2016, this study used the trend extrapolation method to scientifically estimate the missing data from 2017 to 2022.

where $yˆnew$ is the estimated value, $yref$ is the observed marine employment in reference year (2016), $λ$ is average annual change in marine employment during 2011–2016, $tnew$ is the sequential year index for the target years.

In summary, the selected variables sufficiently meet the requirements of the SBM model, enabling a robust evaluation of regional marine economic efficiency in China.

This section employs the Super-efficiency SBM model to measure marine economic efficiency across 11 coastal provinces and cities in China, analyzing their temporal and spatial trends. Building on this analysis, the FGM(1,1) model is used to forecast future development trends in marine economic efficiency. The findings derived from these calculations are then discussed, followed by the presentation of policy recommendations.

As a critical link between land-based and marine economies, China’s 11 coastal provinces and municipalities (excluding Hong Kong, Macao, and Taiwan) account for over 90% of the nation’s total marine economic output. Their efficiency evolution patterns not only reflect regional development disparities but also indicate the effectiveness of national marine strategies. The study area is illustrated in Figure 2.

This study employs an undesirable-output Super-SBM model to construct an evaluation framework incorporating capital, labor, and technology as input indicators, along with gross marine product as the output indicator. Based on carefully selected metrics and verified data sources, we utilize MATLAB 2021a to measure the marine economic efficiency of 11 coastal provinces and municipalities from 2011 to 2022. The analysis subsequently reveals the spatiotemporal evolution patterns and regional disparities in China’s marine economic efficiency. To enable cross-period comparisons—since efficiency values calculated using contemporaneous production frontiers vary across periods—this study employs the undesirable-output Super-SBM model in MATLAB 2021a to construct a global production frontier and compute global marine economic efficiency for 2011–2022. Due to space constraints, Table 5 presents the efficiency values, averages, change rates, and rankings for 11 coastal provinces in selected years (2012, 2014, 2016, 2018, 2020, and 2022). By averaging the marine economic efficiency of these provinces from 2011 to 2022, Figure 3 shows the temporal evolution trend.

Table 5 indicates that: (1) From 2011 to 2022, the marine economic efficiency of Shanghai, Jiangsu, Fujian, and Shandong provinces all exceeded 1, indicating these provinces have reached the efficiency frontier. As economically developed regions in China, these provinces required fewer inputs and generated lower carbon emissions while producing equivalent GOP; (2) The marine economic efficiency of Tianjin, Zhejiang and Guangdong also reached 1 in certain years, suggesting their potential to approach the frontier; (3) Liaoning and Guangxi showed relatively lower marine economic efficiency, with average values of 0.432 and 0.237 respectively during 2011–2022. This may be attributed to their relatively backward economic development, requiring more inputs and emitting more CO₂ to produce GOP of equivalent value compared to other provinces.

From a national perspective, Figure 3 reveals significant disparities in China’s marine economic efficiency during the study period (2011–2022), with average efficiency values ranging between 0.4 and 1.3, showing overall fluctuating trends. This fluctuation can be divided into three distinct phases: a clear upward trend from 2011 to 2013 with an average annual growth rate of 4.52%; a fluctuating downward trend from 2013 to 2020 with an average annual decline of 3.92%; and a marked upward trend from 2020 to 2022 with an average annual growth of 5.85%. These phased fluctuations are closely related to adjustments in China’s marine economic policies and changes in the external environment. The growth period from 2011 to 2013 benefited from the strategic layout for marine economy in the national 12th Five-Year Plan, with accelerated coastal port infrastructure construction and implementation of support policies for emerging marine industries, significantly improving resource allocation efficiency. The downward trend from 2013 to 2020 may be attributed to weak global economic recovery and intensified homogenized competition in marine industries, leading to simultaneous input redundancy and output insufficiency in some regions. Notably, the COVID-19 outbreak in 2020 drove marine economic efficiency to its lowest value that year. The recovery from 2020 to 2022 was directly related to the deepening of the “Maritime Power” strategy, accelerated digital transformation, and promotion of green low-carbon development concepts, with increased investment in marine technology innovation boosting the proportion of high-value-added industries and pushing economic efficiency back onto an upward trajectory. In terms of fluctuation amplitude, the growth rate in the third phase (5.85%) exceeded that of the first phase (4.52%), indicating initial results from China’s high-quality development path for marine economy, though potential risks from changing international situations and regional development imbalances still require vigilance.

Based on the marine economic efficiency values of 11 coastal provinces in China from 2011 to 2022 and referencing previous research findings, we classified marine economic efficiency into five levels: low level (0–0.4), relatively low level (0.4–0.6), medium level (0.6–0.8), relatively high level (0.8–1), and high level (>1). Using 2011 as the baseline with 4-year intervals, we employed ArcGIS software to map the spatial distribution of marine economic efficiency in coastal areas, revealing the spatial evolution characteristics as shown in Figure 4.

Figure 4 shows that in the initial research period, the 11 coastal provinces exhibited distinct spatial gradient differences in marine economic efficiency. The high-level regions included four eastern coastal provinces: Shanghai, Jiangsu, Fujian, and Shandong, which enjoyed first-mover advantages due to their geographical location, port trade, and industrial foundation, with Shandong’s efficiency value at 1.234 and Fujian’s at 1.150. Zhejiang was the only relatively high-level region with balanced marine industry development. Liaoning and Hainan fell into the relatively low-level category, affected by industrial restructuring and limited development, respectively. The low-level regions included Tianjin, Hebei, and Guangxi, characterized by late industrial initiation, small scale, and low resource utilization efficiency.

By 2014, the spatial pattern of marine economic efficiency had changed. The high-level regions remained concentrated in eastern coastal areas, including Tianjin and Hebei, benefiting from infrastructure construction and industrial upgrading. Zhejiang maintained its relatively high-level status. Liaoning and Hainan remained in the relatively low-level category due to over-reliance on traditional sectors, including Liaoning’s shipbuilding industry and Hainan’s dual fishing-tourism economy. Guangxi’s single-industry structure, dominated by fisheries and low-value agricultural sectors, further constrained its development.

In 2018, new characteristics emerged in the spatial distribution of marine economic efficiency. The number of high-level regions decreased to just Shanghai, Jiangsu, Fujian, and Shandong, with efficiency values declining in some areas. The relatively high-level regions increased to three, with Zhejiang, Guangdong, and Tianjin achieving results in technological innovation and industrial integration. Liaoning entered the medium-level category through increased investment and technological innovation. Hainan and Hebei dropped to the relatively low-level group, with Hebei showing a significant efficiency decline. Guangxi remained in the low-level category with no improvement in its development lag.

By 2022, the spatial pattern of marine economic efficiency had further optimized. The high-level regions included Tianjin, Shanghai, Jiangsu, Fujian, and Shandong, leading in high-end and green industrial development. Zhejiang and Guangdong constituted the relatively high-level regions, improving quality through digital economy integration, establishing marine big data platforms, and smart logistics systems. Liaoning and Hainan entered the medium-efficiency category due to targeted industrial policies: Liaoning developed deep-sea equipment clusters, while Hainan leveraged its free trade port status to promote cruise tourism. Hebei’s low efficiency persisted due to structural inertia, with heavy chemical industries comprising a high proportion of its economy. Guangxi continued to lag due to infrastructure gaps, including underdeveloped port logistics and limited high-tech investment.

Through the spatial evolution from 2011 to 2022, regional development imbalances persist. Eastern regions benefited from early industrial agglomeration and policy prioritization, such as the Yangtze River Delta integration strategy. Central regions like Tianjin and Hebei rose through state-led infrastructure investments but faced challenges from traditional industry dominance. Northeastern regions like Liaoning improved via post-industrial revitalization policies, while western regions like Guangxi and Hainan lagged due to resource endowment limitations—such as scarce deep-water ports and reliance on primary industries—and weak technological spillover effects.

After analyzing the spatiotemporal evolution characteristics of China’s coastal marine economic efficiency, this section further investigates its future development trends. The FGM(1,1) model is used to forecast input-output indicators for 2023–2030. Based on these projections, the marine economic efficiency of the 11 coastal provinces from 2023 to 2030 is calculated, with results shown in Table 6 and Figure 5.

The forecast data indicates a general upward trend in marine economic efficiency across the 11 coastal provinces during 2023–2030. The overall average efficiency increases from 0.812 in 2023 to 0.904 in 2030, with an average annual growth rate of 1.58%, though significant regional disparities exist. This phenomenon reflects the varying adaptive capacities of different regions during China’s transition in marine economic development. High-growth regions such as Shanghai, Fujian, and Tianjin should leverage their strategic positioning and policy advantages. These regions could utilize strong research and development ecosystems and institutional innovation to integrate big data and artificial intelligence into port logistics and marine industries. They could also benefit from industrial clustering effects in high-value sectors like marine biotechnology and smart shipping. Conversely, low-growth regions, including Hebei, Guangxi, and Hainan, face constraints due to their reliance on traditional sectors like offshore fishing and low-end tourism. Such regions exhibit weak industrial diversification and limited innovation capacity to transition beyond resource extraction. Priority transformation areas should include accelerating the phase-out of overcapacity in coastal heavy industry.

The spatial distribution of marine economic efficiency in coastal areas is shown in Figure 6.

As shown in Figure 6, the evolution of marine economic efficiency across regions exhibits distinct characteristics. In 2024, among the 11 coastal provinces and municipalities, Shanghai, Fujian, Tianjin, Jiangsu, and Zhejiang, located in the eastern coastal region-demonstrated high efficiency levels, forming a high-efficiency agglomeration zone centered on the Yangtze River Delta and the Bohai Rim. Shanghai maintained its lead by leveraging its international shipping center status and scientific innovation resources, Fujian through marine high-tech industries, Tianjin via smart port upgrades, while Jiangsu and Zhejiang relied on clustered development of marine engineering equipment and biopharmaceutical industries. Shandong and Guangdong remained at relatively lower levels. Shandong was constrained by the delayed transformation of traditional marine industries and insufficient innovation investment, while Guangdong’s underperformance stemmed from intensified competition in the Pearl River Delta port cluster and dispersed emerging industry layout. Liaoning, Hebei, Guangxi, and Hainan were at the lowest tier: all facing weak marine industrial foundations.

By 2030, Shanghai, Fujian, Tianjin, Jiangsu, Zhejiang, and Liaoning will have reached high efficiency levels. While eastern core regions continued leading, Liaoning achieved breakthrough growth through marine renewable energy and equipment manufacturing upgrades, becoming Northeast China’s only high-efficiency region. Guangdong remained at a medium level due to unresolved “large but not strong” marine economy issues and industrial hollowing-out in the Pearl River Delta, whereas Shandong dropped to the low tier from persistent overreliance on traditional industries and inadequate tech investment. Hebei, Guangxi, and Hainan stayed at the bottom, constrained by resource limitations, talent shortages, and inefficient policy implementation. The overall pattern reveals persistent eastern dominance with Liaoning’s leapfrog growth, while most central-western regions formed efficiency depressions due to delayed transformation or weak foundations, exacerbating regional disparities.

Based on the analysis results, this paper proposes the following policy recommendations:

1. High-efficiency regions (Shanghai, Fujian, Tianjin, Jiangsu, Zhejiang, Liaoning by 2030 should focus on promoting technological spillover and regional cooperation. Given their advanced R&D capabilities and well-established industrial clusters, they can establish technology transfer platforms to share innovative achievements with neighboring regions. For example, Shanghai can collaborate with Jiangsu and Zhejiang to form a marine technology innovation alliance, facilitating the flow of talent, capital, and technology. Additionally, these regions should engage in international cooperation, participating in global marine technology research projects and attracting high-end international marine enterprises, thereby enhancing their global competitiveness and driving innovation in the broader marine economy.

2. In regions with medium efficiency, like Guangdong, policies should prioritize industry integration and innovation-driven development. The government can strengthen the integration of the marine industry, promoting cooperation between different sectors to form a more complete industrial chain. By investing in key R&D areas such as marine intelligent manufacturing and marine environmental monitoring technologies, it can improve the added value of its marine products and services.

3. For regions with declining or persistently low efficiency, like Hebei, Guangxi, and Hainan, investment in basic infrastructure is the first step towards development. Hebei and Guangxi should improve port facilities, transportation networks, and energy supply systems to reduce the cost of marine economic activities. Hainan could combine infrastructure construction with industrial transformation. For instance, it can build industrial parks specialized in marine new energy and equipment manufacturing to support the development of high-end marine tourism.

This paper employs a super-SBM model with undesirable outputs to calculate the marine economic efficiency of 11 coastal provinces in China from 2011 to 2022. On this basis, by constructing a grey prediction model to forecast the trend of changes in its internal mechanism, it predicts the marine economic efficiency from 2023 to 2030. The main conclusions are as follows:

1. From 2011 to 2022, significant regional disparities existed in China’s marine economic efficiency. Economically developed eastern coastal provinces such as Shanghai reached the efficiency frontier, while regions like Liaoning and Guangxi showed lower efficiency. Their fluctuations were closely related to adjustments in marine economic policies and changes in the external environment.

2. From the prediction results, the marine economic efficiency of 11 coastal provinces and cities is expected to show an overall upward trend from 2023 to 2030, with an average annual growth rate of 1.58%. However, regional disparities remain large: areas with strong innovation foundations will continue to improve, while regions dependent on traditional resources may experience sluggish or declining efficiency, highlighting the necessity and urgency of technology-driven transformation.

3. Despite the projected national efficiency average rising to 0.904 by 2030, regional divergence is intensifying. While eastern regions maintain leadership through digital/green transitions, central-western areas struggle with weak industrial foundations and policy implementation delays, underscoring the pressing need to shift from factor-driven to innovation-driven marine economic development.

Research indicates that to promote high-quality marine economic development, it is essential to prioritize scientific and technological innovation and regional coordination. High-efficiency regions should solidify their advantages, and low-efficiency regions should overcome transformation bottlenecks.

The research conclusions are primarily applicable to regions facing similar development challenges, particularly in industrial transformation, resource allocation, and technological innovation. It can provide references for coastal areas in developing countries that aim to upgrade their marine economies. The implementation requires tailoring to local political, economic, and social contexts.