18th International Conference on Innovative Computing, Information and Control (ICICIC2024)
Onsite (Qinhuangdao, China) and Online, September 10-13, 2024

PS-19: ICICIC2024-WS05-19
Study on the Efficiency Evaluation of Port Green Technology Innovation
Based on Ultra-Efficiency DEA and DEA-Malmquist Index Model
1

1,

2

Shicheng Li , Sili Wang * and Linshan Li
1

School of Economics and Management (School of Tourism), Dalian University, Dalian 116622, P. R. China
2Business School, Dalian Minzu University, Dalian 116650, P. R. China
lishicheng0425@163.com; *Corresponding author: wangsili@msn.cn

IntroductionMethodologies

China’s current economy is shifting from high-speed
growth to high-quality development, which is a deep-seatedadjustment of the concept and model of development. In
this process, green development is particularly importantand has become a key direction for economictransformation and upgrading. This is not only in line with
the needs of domestic sustainable development, but also inline with the trend of global green economy.
China’s economy is transitioning from rapid growth toquality-focused development, reflecting a profound shift indevelopment philosophy. Green development is crucial inthis shift, aligning with domestic sustainability needs andglobal green economy trends.
The “green” goal is essential for implementing the newdevelopment concept, promoting high-quality development,
and balancing economic growth with environmental
protection. Environmental degradation makes green
technology innovation crucial for China to address resourceand environmental constraints.
The Data Envelope Analysis (DEA) model measures
the relative efficiency of decision-making units. However,traditional DEA models have limitations when evaluating
port efficiency, lacking depth in analyzing efficiency gaps
and conducting static analysis. To address these issues, asuper-efficiency CCR-DEA (Charnes-Cooper-Rhodes-Data
Envelope Analysis) mode is introduced to evaluate theefficiency of ports achieving an effective state. The DEAMalmquist index model shows port efficiency changes overyears, and total factor productivity decomposition identifies
the root causes of these changes.
The organization of this paper focuses on the study ofsustainable development in the port industry. Greenindicators are incorporated into the port efficiency
evaluation system, combined with the super-efficiency DEA
and DEA-Malquist index mode for static and dynamic
analysis, respectively, to measure the green technologicalinnovation efficiency of 10 ports, in order tocomprehensively assess the efficiency status of green
technological innovation in ports. The calculation results arealso analyzed in depth to find out the important factors
affecting the efficiency of green technological innovation inports, and analyze the inherent pattern of change. Finally,
based on the results of the above analysis, suggestions areput forward, aiming to provide strong theoretical support anddecision-making reference for the sustainable developmentof China’s ports.
The core contribution of this study is to conduct asystematic and in-depth research on the efficiency of green
technology innovation by using the super-efficiency DEA
model and DEA-Malmquist index model analysis with theport industry as the key research object.

1) Ultra-efficiency DEA model.
The ultra-efficiency DEA model is a powerful tool
to evaluate the efficiency of enterprise technology
innovation. It is difficult to evaluate the advantages
and disadvantages of traditional DEA models when
multiple decision units show efficiency. To answer thisproblem, Anderson and Petersen (1993) proposed the
input-oriented ultra-efficiency CCR-DEA model. The
efficiency value evaluation result of the “non-DEAeffective” decision unit is the same as that of the
traditional DEA model, but the efficiency value of the“DEA effective” decision unit is higher than themeasurement results of the traditional DEA model.
2) DEA-Malmquist index model.
The Malmquist index was proposed by
MALMQUIST in 1953 to analyze the growth of totalfactors at two different time points. In 1994, FÄRE etal. combined it with the DEA method to decompose
total factor productivity into efficiency changes andtechnological changes. The DEA-Malmquist index
evaluates the dynamic productivity of different time
periods by calculating input-output efficiency. Basedon the input vector, output vector and distancefunction values, it calculates the Total Factor
Productivity
ChangeIndex(TFPCH),
theTechnological Efficiency Change Index (EFFCH), theTechnological Progress Change Index (TECHCH), theScale Efficiency Change Index (SECH) and the Pure
Technology Efficiency Change Index (PECH).

Mathematical Formulas
Minimizeθ
Subject ton∑ xi λ ≤ θx , i = 1,2..., mj =1j ≠ojjion∑ y λ ≥ y ,1,2,..., s,j =1j ≠0rjjro

λ j ≥ 0, j ≠ o.
d (x , y ) d (x , y ) 1
m0 ( xt +1 , yt +1 , xt , yt ) = [ t t +1 t +1 * t +1 t +1 t +1 ] 2
d t ( xt , yt )d t +1 ( xt , yt )(3)

I tfpch = I effch × I tech， I effch = Isech × I pech， I tfpch = I tech × I sech × I pech(4)

18th International Conference on Innovative Computing, Information and Control (ICICIC2024)
Onsite (Qinhuangdao, China) and Online, September 10-13, 2024

Research Questions
This paper explores the efficiency of green technology innovation in China’s ports and analyzes the factors influencing
the green technology innovation of port enterprises.

TablesTable 1. Ultra-efficiency values of China’s top ten ports from 2015 to 2022
Port

Mean2015

2016

2017

2018

2019

2020

2021

2022

Yantian port

1.065

1.121

1.997

0.892

0.862

0.867

1.068

0.904

0.806

Beibu gulf port

0.130

0.160

0.157

0.142

0.161

0.094

0.103

0.111

0.113

Xiamen port affairs

0.116

0.137

0.109

0.104

0.110

0.110

0.113

0.123

0.120

Rizhao port

0.208

0.157

0.161

0.177

0.222

0.216

0.229

0.244

0.260

Sipg

0.226

0.098

0.093

0.113

0.155

0.138

0.301

0.438

0.468

Port of tianjin

0.332

0.337

0.189

0.173

0.500

0.192

0.507

0.494

0.261

Port of tongxhan

0.519

0.463

0.596

0.676

0.415

0.446

0.484

0.515

0.554

Lianyungang

0.192

0.162

0.191

0.143

0.159

0.180

0.190

0.226

0.281

Dalian port

0.184

0.182

0.172

0.170

0.174

0.176

0.173

0.215

0.207

Ningbo zhoushan port

0.158

0.153

0.156

0.164

0.177

0.144

0.142

0.151

0.179

0.297

0.382

0.275

0.293

0.256

0.331

0.342

0.325

Mean

FigureFigure 1. The techch index of China’s top ten major ports

Figure 2. The Malmquist index of China’s top ten major ports

Conclusion
From a static point of view, the overallinnovation efficiency of green technology inChina’s ports is low, but it maintains asustained and slow growth trend.
From a dynamic perspective, theefficiency of green technology innovation in
China’s ports has maintained a relatively
stable trend, indicating that with thedevelopmentofports,
thegreentechnology innovation in ports is also
making continuous progress. In thisprocess, the TFPCH index of each port issignificantly influenced by the Technical
Progress Index, while the pure technicalefficiency Index also plays a crucial role.