Assessing Safety Efficiency in China’s Provincial Construction Industry: Trends, Influences, and Implications
Abstract
:1. Introduction
2. Literature Review
2.1. Evaluation of Construction Safety Efficiency
2.2. Analysis of Factors Influencing Safety Efficiency
3. Methodology
3.1. Super-SBM Model
3.2. Malmquist Index
3.3. Panel Quantile Regression Model
4. Index Selection and Data Source
4.1. Index Selection
4.1.1. Selection of Input–Output Indicators
4.1.2. Selection of Influencing Factors Indicators
4.2. Data Sources
5. Results
5.1. Static Analysis of Safety Efficiency Results
5.1.1. Temporal Evolution Characteristics
5.1.2. Spatial Evolution Characteristics
5.2. Dynamic Analysis of Safety Efficiency Results
5.3. Analysis of Influencing Factors
5.3.1. Descriptive Statistics of Variables
5.3.2. Quantile Regression Results Analysis
6. Discussion
- (1)
- Some provinces show better performance in safety efficiency, such as Beijing and Hainan. However, the overall level of construction safety efficiency is relatively low, with most provinces’ efficiency values ranging from 0.2 to 0.6. A similar conclusion was drawn by Kang et al. [25]. However, the efficiency values obtained in this study are even lower, indicating an overestimation issue with the traditional DEA model. The data obtained using the Super-SBM model are closer to the actual values. Several reasons contribute to the low level of safety efficiency in China: firstly, the construction industry has witnessed a substantial surge in growth in recent years, particularly in the context of intensified competition in the real estate market. This has led to enterprises prioritizing scale expansion while overlooking resource utilization optimization, emphasizing profit-driven and unsustainable expansion. Secondly, as the cost of accidents rises with the country’s increased supervision of building safety, there has been a reduction in unsafe behaviors to some extent, subsequently decreasing the probability of accidents. However, the extent of improvement remains inadequate, necessitating the strengthening of further measures, as incomplete safety measures result in a decline in safety efficiency.
- (2)
- Similarly, the dynamic change results show that the total factor productivity index was consistently below 1 over the course of 7 years, indicating a declining trend in safety efficiency in the Chinese construction industry. This result has not been described previously. The lowest total factor productivity index was observed in 2015–2016, at 0.924. One possible reason for this situation is that in 2015, China’s macroeconomic conditions experienced a downturn, with continuous sluggishness in real estate investment and building material prices frequently hitting rock bottom. This challenging environment led to a historic low in the growth rate of the construction industry’s total output value, posing significant challenges to the survival and development of the construction industry. Under such circumstances, enterprises had limited bandwidth to focus on enhancing their technical capabilities and improving management efficiency. Consequently, there was an overall decline in both technological progress efficiency and technical efficiency during this period. This decline, in turn, contributed to a decrease in safety efficiency.
- (3)
- Then, we find that the influence coefficient and significance of each factor change with the change in different quantiles of safety efficiency, indicating that the influence of determinants of safety efficiency is heterogeneous among different quantiles. However, this result has not been described before. Relevant decision-making departments should consider the differences in the driving forces of safety efficiency in different provinces and formulate policies reasonably. The impact of the urbanization rate on building safety efficiency shows a negative downward trend, indicating that the negative impact of the urbanization rate on the low safety efficiency level is the least, while the negative impact on the high safety efficiency level is relatively large. This may be caused by the rate of urbanization, which causes rural labor to flood into cities. While providing abundant labor resources for the construction industry, the complexity of safety management faced by the construction industry increases, increasing the risk of safety accidents.
7. Conclusions
- (1)
- The results of the Super-SBM model indicate that the overall safety efficiency of provincial-level construction in China is relatively low, with only a few provinces showing good safety efficiency, such as Beijing, Jiangsu, and Hainan. There is a significant spatial clustering feature, with higher safety efficiency levels observed in eastern and central provinces. High-efficiency provinces are mainly distributed in the central and eastern regions and coastal areas, exhibiting a phenomenon of mutual connection between low-efficiency provinces and medium–high-efficiency regions.
- (2)
- The decomposition results of the Malmquist index show a downward trend from 2015 to 2021, with technological efficiency being the main factor hindering the development of safety efficiency in enterprises. Except for Beijing, Tianjin, Inner Mongolia, Shanghai, Jiangsu, and Gansu, the total factor productivity (TFP) of each province has shown a declining trend. The changes in technological progress and technical efficiency vary in different provinces, so different regions should improve and optimize technological progress, technical efficiency, and management levels according to their local conditions to promote the improvement of construction safety efficiency.
- (3)
- The results of the panel quantile regression show that per capita GDP, urbanization rate, contract amount, and the number of professionals engaged in survey and design, and engineering supervision have an impact on construction safety efficiency. Specifically, regardless of whether safety efficiency is low (0.25), medium (0.5), relatively high (0.75), or high (0.9), per capita GDP and urbanization rate have a significant impact on safety efficiency; the negative impact of the contract amount is most pronounced at the 0.9 quantile; the number of professionals in survey and design has the most significant impact at the 0.5 quantile; and the number of engineering supervision professionals has the strongest impact at the 0.75 quantile.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Type | Specific Indicators | Specific Measurement |
---|---|---|
Input | Safe investment | Gross output value of regional construction industry × safety input rate (2.36%) |
Number of employees in construction industry | Number of employees in general contracting and professional contracting construction enterprises | |
Unexpected output | Number of construction safety accidents | Number of safety accidents in housing municipal engineering |
Number of deaths from construction safety accidents | Number of deaths in housing municipal engineering safety accidents | |
Expected output | Construction area of construction industry | Housing construction area of general contracting and professional contracting construction enterprises |
Category | Specific Indicators |
---|---|
Economic and social development | Per capita GDP (PER) |
urbanization rate (URB) | |
Auxiliary industry development | Number of employees in exploration and design institutions (EXP) |
Number of employees in engineering supervision institutions (SPV) | |
Construction industry development | Technical equipment rate of construction industry (EQU) |
Labor productivity of construction industry (LAB) | |
Contract value of construction industry (CON) |
Province | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Mean | Rank |
---|---|---|---|---|---|---|---|---|---|
Beijing | 1.053 | 0.861 | 0.828 | 0.891 | 1.002 | 1.051 | 1.834 | 1.074 | 1 |
Tianjin | 0.343 | 0.354 | 0.375 | 0.357 | 0.364 | 0.358 | 0.415 | 0.366 | 28 |
Hebei | 0.587 | 1.102 | 0.795 | 0.552 | 0.962 | 1.781 | 0.742 | 0.932 | 5 |
Shanxi | 1.038 | 1.032 | 0.413 | 0.388 | 0.360 | 0.395 | 0.484 | 0.587 | 12 |
Inner Mongolia | 0.607 | 0.525 | 0.505 | 0.534 | 0.601 | 0.714 | 0.745 | 0.604 | 10 |
Liaoning | 0.459 | 0.442 | 0.394 | 0.364 | 0.417 | 0.428 | 0.494 | 0.428 | 25 |
Jilin | 0.496 | 0.437 | 0.418 | 0.430 | 0.441 | 0.471 | 0.438 | 0.447 | 24 |
Heilongjiang | 0.328 | 0.342 | 0.336 | 0.351 | 0.348 | 0.358 | 0.406 | 0.353 | 29 |
Shanghai | 0.585 | 0.552 | 0.616 | 0.666 | 0.664 | 0.690 | 0.663 | 0.634 | 8 |
Jiangsu | 1.016 | 1.001 | 0.998 | 1.010 | 1.006 | 1.024 | 1.025 | 1.011 | 3 |
Zhejiang | 0.905 | 0.870 | 0.859 | 0.821 | 1.129 | 1.099 | 0.967 | 0.950 | 4 |
Anhui | 0.660 | 0.563 | 0.556 | 0.511 | 0.495 | 0.462 | 0.458 | 0.529 | 16 |
Fujian | 0.701 | 0.637 | 0.532 | 0.514 | 0.474 | 0.477 | 0.456 | 0.541 | 14 |
Jiangxi | 0.530 | 0.467 | 0.430 | 0.426 | 0.382 | 0.360 | 0.339 | 0.419 | 26 |
Shandong | 1.000 | 0.630 | 0.582 | 0.543 | 0.507 | 0.513 | 0.567 | 0.620 | 9 |
Henan | 0.561 | 0.528 | 0.465 | 0.482 | 0.435 | 0.436 | 0.548 | 0.494 | 19 |
Hubei | 0.501 | 0.529 | 0.513 | 0.530 | 0.531 | 0.501 | 0.521 | 0.518 | 17 |
Hunan | 0.627 | 0.576 | 0.541 | 0.520 | 0.512 | 0.491 | 0.501 | 0.538 | 15 |
Guangdong | 0.488 | 0.485 | 0.457 | 0.469 | 0.446 | 0.459 | 0.536 | 0.477 | 21 |
Guangxi | 0.779 | 0.715 | 0.512 | 0.506 | 0.471 | 0.440 | 0.410 | 0.548 | 13 |
Hainan | 1.586 | 0.954 | 0.935 | 0.946 | 0.910 | 0.909 | 1.033 | 1.039 | 2 |
Chongqing | 0.441 | 0.385 | 0.369 | 0.401 | 0.381 | 0.369 | 0.340 | 0.384 | 27 |
Sichuan | 1.006 | 0.455 | 0.424 | 0.397 | 0.361 | 0.368 | 0.364 | 0.482 | 20 |
Guizhou | 0.968 | 0.876 | 0.545 | 0.450 | 0.400 | 0.580 | 0.391 | 0.601 | 11 |
Yunnan | 0.413 | 0.383 | 0.314 | 0.319 | 0.292 | 0.278 | 0.251 | 0.322 | 30 |
Shanxi | 0.527 | 0.546 | 0.509 | 0.377 | 0.409 | 0.411 | 0.387 | 0.452 | 23 |
Gansu | 0.521 | 0.486 | 0.482 | 0.511 | 0.515 | 0.503 | 0.531 | 0.507 | 18 |
Qinghai | 0.682 | 0.664 | 0.680 | 0.695 | 0.761 | 0.805 | 1.033 | 0.760 | 7 |
Ningxia | 0.745 | 1.215 | 0.865 | 0.622 | 0.579 | 0.810 | 0.539 | 0.768 | 6 |
Xinjiang | 0.555 | 0.517 | 0.428 | 0.414 | 0.381 | 0.471 | 0.492 | 0.466 | 22 |
Mean | 0.690 | 0.638 | 0.556 | 0.533 | 0.551 | 0.600 | 0.597 | 0.595 |
Year | EC | TC | PEC | SEC | MI |
---|---|---|---|---|---|
2015~2016 | 0.961 | 0.961 | 0.987 | 0.974 | 0.924 |
2016~2017 | 0.936 | 0.997 | 0.886 | 1.056 | 0.933 |
2017~2018 | 0.986 | 1.007 | 1.150 | 0.857 | 0.993 |
2018~2019 | 0.919 | 1.068 | 0.881 | 1.043 | 0.982 |
2019~2020 | 0.989 | 1.005 | 0.987 | 1.002 | 0.994 |
2020~2021 | 1.043 | 0.960 | 1.073 | 0.971 | 1.001 |
Mean | 0.972 | 0.999 | 0.990 | 0.982 | 0.971 |
Province | EC | TC | PEC | SEC | MI |
---|---|---|---|---|---|
Beijing | 0.968 | 1.175 | 0.984 | 0.984 | 1.137 |
Tianjin | 1.034 | 0.993 | 1.045 | 0.989 | 1.027 |
Hebei | 0.991 | 0.995 | 1.143 | 0.867 | 0.986 |
Shanxi | 0.996 | 0.996 | 0.957 | 1.041 | 0.992 |
Inner Mongolia | 1.019 | 0.995 | 1.046 | 0.974 | 1.014 |
Liaoning | 0.998 | 0.995 | 1.168 | 0.855 | 0.993 |
Jilin | 0.969 | 0.995 | 1.008 | 0.961 | 0.964 |
Heilongjiang | 1.001 | 0.995 | 1.054 | 0.950 | 0.997 |
Shanghai | 1.011 | 0.994 | 1.014 | 0.998 | 1.006 |
Jiangsu | 1.074 | 0.939 | 0.984 | 1.092 | 1.009 |
Zhejiang | 0.991 | 1.009 | 1.014 | 0.977 | 1.000 |
Anhui | 0.964 | 0.995 | 0.967 | 0.997 | 0.959 |
Fujian | 0.959 | 0.984 | 0.960 | 0.999 | 0.944 |
Jiangxi | 0.937 | 0.995 | 0.935 | 1.003 | 0.932 |
Shandong | 0.942 | 1.031 | 0.943 | 0.999 | 0.972 |
Henan | 0.967 | 0.993 | 1.034 | 0.935 | 0.961 |
Hubei | 0.997 | 0.995 | 0.998 | 1.000 | 0.992 |
Hunan | 0.978 | 1.000 | 0.961 | 1.018 | 0.977 |
Guangdong | 0.997 | 0.994 | 0.997 | 1.000 | 0.991 |
Guangxi | 0.929 | 0.994 | 0.961 | 0.967 | 0.924 |
Hainan | 0.913 | 0.995 | 0.740 | 1.233 | 0.909 |
Chongqing | 0.967 | 0.994 | 0.972 | 0.996 | 0.962 |
Sichuan | 0.901 | 0.965 | 0.898 | 1.002 | 0.869 |
Guizhou | 0.900 | 0.996 | 0.948 | 0.949 | 0.896 |
Yunnan | 0.927 | 0.995 | 0.936 | 0.990 | 0.922 |
Shanxi | 0.981 | 0.996 | 0.991 | 0.990 | 0.978 |
Gansu | 1.010 | 0.994 | 1.026 | 0.985 | 1.005 |
Qinghai | 0.977 | 0.996 | 1.077 | 0.908 | 0.973 |
Ningxia | 0.897 | 0.994 | 0.981 | 0.915 | 0.892 |
Xinjiang | 0.977 | 0.993 | 1.034 | 0.945 | 0.970 |
Mean | 0.972 | 0.999 | 0.990 | 0.982 | 0.971 |
Variable | Mean | StDev | Min | Max |
---|---|---|---|---|
TE | 0.595 | 0.256 | 0.251 | 1.834 |
PER | 64,456 | 31,091 | 25,264 | 183,980 |
URB | 62.20 | 10.81 | 42.93 | 89.30 |
CON | 163,700,000 | 140,900,000 | 6,129,000 | 614,400,000 |
LAB | 400,197 | 110,232 | 228,326 | 761,375 |
EQU | 13,273 | 10,542 | 3723 | 108,588 |
EXP | 137,335 | 148,880 | 5021 | 683,409 |
SPV | 40,659 | 32,752 | 3712 | 210,047 |
Variable | Coefficients | |||
---|---|---|---|---|
θ = 0.25 | θ = 0.5 | θ = 0.75 | θ = 0.9 | |
PER | 0.465 *** | 0.845 *** | 1.262 *** | 1.751 ** |
URB | −0.357 *** | −0.531 *** | −0.84 *** | −1.037 * |
CON | −0.096 | −0.051 | −0.598 | −1.048 * |
LAB | 0.037 | −0.139 | −0.02 | −0.009 |
EQU | 0.054 | 0.078 | −0.024 | −0.096 |
EXP | 0.222 | 0.391 ** | 0.581 * | 0.333 |
SPV | −0.152 | −0.357 | −0.212 * | 0.375 |
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Wang, X.; Zhao, B.; Su, C. Assessing Safety Efficiency in China’s Provincial Construction Industry: Trends, Influences, and Implications. Buildings 2024, 14, 893. https://doi.org/10.3390/buildings14040893
Wang X, Zhao B, Su C. Assessing Safety Efficiency in China’s Provincial Construction Industry: Trends, Influences, and Implications. Buildings. 2024; 14(4):893. https://doi.org/10.3390/buildings14040893
Chicago/Turabian StyleWang, Xinping, Boxi Zhao, and Chang Su. 2024. "Assessing Safety Efficiency in China’s Provincial Construction Industry: Trends, Influences, and Implications" Buildings 14, no. 4: 893. https://doi.org/10.3390/buildings14040893