Inuencing factors, scenario prediction and decoupling ...

In�uencing factors, scenario prediction anddecoupling effect of carbon intensity of the wholeprocess of agricultural products logisticsXueru Fan ( [email protected] )

Jiangsu UniversityGuanxin Yao

Yangzhou UniversityDongmei Zhang

Jiangsu UniversityXiaoyu Bian

Jiangsu University

Research Article

Keywords: The whole process of agricultural product logistics, Carbon intensity, LMDI decompositionmethod, STIRPAT model, Quantity decoupling model, Scenario prediction

Posted Date: September 3rd, 2021

DOI: https://doi.org/10.21203/rs.3.rs-722065/v1

License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License

https://doi.org/10.21203/rs.3.rs-722065/v1

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Influencing factors, scenario prediction and decoupling effect of carbon intensity of the whole 1

process of agricultural products logistics 2

Xueru Fan1·Guanxin Yao2·Dongmei Zhang1·Xiaoyu Bian1 3

4

Abstract 5

The reduction of carbon intensity of the whole process of agricultural products logistics is of great 6

significance to the comprehensive control of China's carbon intensity. LMDI decomposition method, 7

STIRPAT model and quantitative decoupling analysis model are used to study the influencing factors, 8

future development scenarios and decoupling effect with economic development of the whole process 9

carbon intensity of agricultural products logistics in China from 2000 to 2017. The countermeasures 10

and suggestions to reduce the carbon intensity of the whole process of agricultural products logistics in 11

China are put forward based on the research results of influencing factors, scenario prediction and 12

decoupling effect. 13

14

Keywords The whole process of agricultural product logistics·Carbon intensity·LMDI decomposition 15

method·STIRPAT model·Quantity decoupling model·Scenario prediction 16

17

Xueru Fan 18

[email protected] 19

Guanxin Yao 20


Dongmei Zhang 22


Xiaoyu Bian 24


26

1 School of Management, Jiangsu University, Zhenjiang 212013, People’s Republic of China 27

2 Jiangsu Modern Logistics Research Base, Yangzhou University, Yangzhou 225009, People’s 28

Republic of China 29

30

1 Introduction 31

Reducing carbon intensity is the first choice of energy saving and emission reduction policy in China 32

under the background of global warming. China's overall carbon intensity has decreased by about 33

48.4% compared with 2005 to the end of 2020. Carbon intensity refers to the carbon dioxide emission 34

per unit of GDP, also known as carbon dioxide emission intensity. China proposes that the carbon 35

intensity should be reduced by 60% - 65% by 2030 compared with 2005 in the Intended Nationally 36

Determined Contributions (INDCs) at the 21st United Nations Climate Change Conference. In addition, 37

China has also proposed to accelerate green and low-carbon development and reduce carbon emission 38

intensity in the 14th Five-Year Plan (2020) of the CPC Central Committee. 39

Agricultural products logistics is an important bridge and link between urban and rural areas, 40

production and consumption. It is necessary to realize its low-carbon construction to comprehensively 41

promote the reduction of carbon intensity in China. China's agricultural products logistics is in a critical 42

period of transformation from extensive operation to low-carbon operation at present (Zhang et al. 43

2020). It is urgent to alleviate the problems of high energy consumption, high emission and high cost, 44

control the carbon intensity of agricultural products logistics, and promote the reduction of China's 45

overall carbon intensity. There are abundant theoretical achievements in the research of low-carbon 46

agricultural products logistics (Yao et al. 2018; Ning et al. 2021; Yuan et al. 2020). But most of them 47

focus on the absolute reduction of carbon emissions as well as the reduction of a certain process, such 48

as transportation, storage and packing. The whole process perspective and carbon intensity control 49

research are very few in the field of low carbon research of agricultural products logistics. It is of 50

theoretical value and practical significance to study the carbon intensity control in the whole process of 51

agricultural products logistics. Therefore, the influencing factors, future development scenarios and 52

decoupling effect with economic development of the carbon intensity of the whole process of 53

agricultural products logistics will be discussed in this study. The whole process perspective will be 54

considered, including transportation, storage and packaging processes, which have the common 55

characteristics of high emission, high energy consumption and high cost (Kang et al. 2019; Wang et al. 56

2017; Jiang et al. 2017). The data of energy consumption, carbon emission and output value of the 57

whole process of agricultural products logistics from 2000 to 2017 will be adopted, and LMDI 58

decomposition method, STIRPAT model and quantity decoupling model will be applied. Finally, it will 59

provide a scientific theoretical basis for the control of carbon intensity of the whole process of 60

agricultural products logistics in reality through empirical research and suggestions. 61

The rest of the article is as follows. Section 2 analyzes the researches of low-carbon of the whole 62

process of agricultural products logistics in recent years. Section 3 introduces the research methods and 63

data sources. Section 4 deals with the data, makes an empirical analysis of carbon intensity of the 64

whole process of agricultural products logistics and discusses the research results. Section 5 65

summarizes the conclusions of this work, and puts forward policy suggestions to reduce carbon 66

intensity of the whole process of agricultural products logistics. 67

68

2 Literature review 69

Low carbon development of agricultural products logistics can save energy, reduce emissions and 70

enhance the international competitiveness of China's agricultural products (Guo and Zong 2012), which 71

is not only the inherent requirement of logistics development, but also the inevitable choice of 72

sustainable development of agriculture (Zhang 2011). Relevant research is also very necessary. 73

The logistics of agricultural products in China is characterized by many varieties, large quantity, high 74

cost and low green degree (Yin 2011). Transportation, storage and packaging are the key and difficult 75

points in the low-carbon construction of agricultural products logistics, because these processes lead to 76

more carbon emissions. 77

As far as the research on low-carbon transportation and distribution of agricultural products is 78

concerned, there are some problems in the transportation and distribution of agricultural products, such 79

as too large proportion of road transportation, poor scheduling capacity, many idle and empty loads and 80

slow development of multimodal transport, which lead to large carbon emissions (Sun and Yang 2014). 81

Therefore, it is necessary to optimize the logistics transportation and distribution system of agricultural 82

products, which cannot only develop market economy and build modern logistics, but also realize 83

sustainable development (Chen et al. 2019). For example, the carbon emission cost is included in the 84

total cost of fresh agricultural products cold chain logistics distribution, and various optimization 85

algorithms (Kang et al. 2019; Chen et al. 2019; Wang et al. 2017; Zhang 2019) are used to solve the 86

distribution path optimization model, so as to realize the low carbonization of fresh agricultural 87

products cold chain transportation and distribution. We can reduce the carbon footprint of consumers 88

by changing road transportation to railway transportation and shortening the supply logistics of dry 89

flour and grain (Cimini et al. 2019). The direct distribution strategy considering carbon emissions can 90

achieve fast fresh delivery, which has higher economic and environmental benefits compared with the 91

existing agricultural products logistics distribution strategy (Kim and Cho 2017). In addition to 92

transportation and distribution, the low-carbon development of agricultural products storage process 93

cannot be ignored. The research on low-carbon development of agricultural products storage mainly 94

focuses on the layout, location, inventory and other practical problems. The layout planning of urban 95

green agricultural products logistics center under the background of low-carbon concept can help 96

promote the low-carbon development of agricultural products logistics (Zhou et al. 2016). From the 97

perspective of packaging, the cycle sharing mode of green packaging and turnover box of agricultural 98

products cannot only realize efficient and low-cost modern logistics, but also realize social and 99

environmental benefits, which is necessary for promotion (Miao et al. 2019). Some scholars also 100

discussed the transportation and storage of agricultural products at the same time. For example, the 101

agricultural product logistics model is constructed, and the location and distribution path scheme with 102

the minimum total logistics cost is selected under the joint constraints of carbon emission, carbon tax 103

and carbon emission trading (Liang et al. 2019). 104

All the above studies focus on carbon emission reduction in the transportation, storage or packaging 105

process of agricultural products logistics. However, it is more scientific to study the carbon intensity 106

index under the current development background, because the environmental benefits and economic 107

benefits in the development process of agricultural products logistics are contrary to each other. 108

Effective control of the carbon intensity of agricultural products logistics can further promote the 109

coordinated development of economy and low carbon of agricultural products logistics. There are few 110

literatures on carbon intensity of agricultural products logistics, and a small number of researches are 111

focused on agricultural products logistics from the perspective of low-carbon economy. The carbon 112

intensity control of agricultural products logistics is to control the carbon emission per unit of 113

agricultural products logistics output value. Agricultural products logistics from the perspective of 114

low-carbon economy is mainly qualitative and simple quantitative research, lack of scientific 115

quantitative index research. For example, some studies put forward development countermeasures and 116

suggestions for energy conservation and emission reduction of agricultural products logistics through 117

macro qualitative analysis, based on the current situation of high emission, high energy consumption 118

and high cost (Jiang et al. 2017; Zhang 2011; Wu and Haasis 2018). For another example, some studies 119

use simple quantitative methods to incorporate carbon emission costs into the operation costs of 120

agricultural products logistics transportation, storage and packaging logistics (Kang et al. 2019; Chen et 121

al. 2019; Wang et al. 2017; Zhang 2019; Kim and Cho 2017; Miao et al. 2019), discuss the 122

development direction of low-carbon economy, and then put forward countermeasures and suggestions 123

considering both low-carbon development and economic construction. 124

To sum up, there are some deficiencies in the research on the low-carbon development of agricultural 125

products logistics, although there are many related results, which lay a theoretical foundation for this 126

research. On the one hand, there is a lack of in-depth discussion on the control of carbon intensity of 127

agricultural products logistics. The existing research on low-carbon or sustainable development of 128

agricultural products logistics mainly focuses on macro theory, current situation and countermeasures, 129

and absolute control of carbon emissions. On the other hand, there is a lack of consideration on the 130

overall and systematic emission reduction of all processes of agricultural products logistics. The 131

process boundary of agricultural products logistics is relatively clear, and the logistics processes such 132

as transportation, storage and packaging are relatively independent but interact with each other. The 133

existing research and practice mostly focus on the low-carbon research of a certain process, which is 134

difficult to realize the real collaborative emission reduction among logistics processes. Therefore, it is 135

of great theoretical value to study the carbon intensity control of agricultural products logistics from the 136

perspective of the whole process. 137

138

3 Methodologies and data sources 139

3.1 Carbon intensity calculation of the whole process of agricultural products logistics 140

3.1.1 Calculation process framework of carbon intensity of the whole process of agricultural products 141

logistics 142

The carbon intensity of the whole process of agricultural products logistics is the carbon dioxide 143

emissions per unit of added value of agricultural products logistics, including agricultural products 144

transportation, storage and packaging. The calculation process framework of carbon intensity of 145

agricultural products logistics will be designed from the perspective of the whole process based on the 146

existing relevant research (Zhu et al. 2010; Yao et al. 2017). The whole process perspective is to fully 147

consider the logistics processes of agricultural products with high emissions, high energy consumption 148

and high cost. Among them, the transportation process of agricultural products mainly includes the 149

construction of transportation facilities, the manufacture of transportation equipment and the 150

development of transportation activities. The process of agricultural products storage mainly includes 151

the construction of storage facilities, the manufacturing of storage equipment and the development of 152

storage activities. The packaging process of agricultural products mainly includes the manufacturing of 153

packaging equipment and the consumption of packaging materials. Accordingly, the calculation flow 154

chart of carbon intensity of the whole process of agricultural products logistics is as follows (Figure 1). 155

156

Fig. 1 Calculation flow chart of carbon intensity of the whole process of agricultural products logistics 157

3.1.2 Calculation method of carbon emission and carbon intensity 158

The calculation formula of carbon emissions of the whole process of agricultural products logistics is 159

as follows. 160

3

1

8

1

3

1

8

1 i j

jjjjij

i j

jjijij EEC (1) 161

Where i = 1,2,3, which means the transportation, storage and packaging process of agricultural 162

products logistics; 163

j = 1,2,..., 8, indicating the energy types of coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil 164

and natural gas, regardless of the calculated power consumption in primary energy consumption; 165

Cij is the carbon emission of the fuel item j in agricultural products logistics process i; 166

Eij is the consumption of the fuel item j in agricultural products logistics process i; 167

μj is the conversion coefficient of standard coal of the fuel item j; 168

ωj is the carbon emission coefficient of the fuel item j; 169

ηj is the carbon emission factor of the fuel item j; 170

δj is the oxidation rate of the fuel item j; 171

γj is the mean low calorific value of the fuel item j. 172

Furthermore, the calculation formula of carbon intensity of the whole process of agricultural products 173

logistics is as follows. 174

Q

C

Ii j

ij

3

1

8

1 (2) 175

Where I is the carbon intensity of the whole process of agricultural products logistics; 176

Q is the added value of agricultural products logistics; 177

i, j and Cij have the same meaning as above. 178

3.2 LMDI decomposition method 179

LMDI decomposition method is used to study the influencing factors of carbon intensity of the whole 180

process of agricultural products logistics. LMDI decomposition method, the logarithmic mean Divisia 181

index method, was proposed by Ang B W (Ang 2005; Ang 2015), which includes two decomposition 182

methods: addition and multiplication. It has the advantages of no residual error term and complete 183

decomposition (Liu et al. 2019), and is widely used in the research of energy, carbon emission and 184

carbon intensity. LMDI decomposition method is applied to decompose carbon intensity of the whole 185

process of agricultural products logistics into six parts, and the formula is as follows. 186

3

1

8

1i j

i

i

ij

ij

ij

Q

P

P

W

W

E

E

E

E

E

E

CI (3) 187

Where Ei is the energy consumption of the agricultural products logistics process i; 188

E is the total energy consumption of the whole process of agricultural products logistics; 189

W is the freight volume of agricultural products; 190

P is GDP; 191

I, i, j, Cij, Eij and Q have the same meaning as above. 192

Therefore, the related coefficient of carbon intensity of the whole process of agricultural products 193

logistics is defined, and the transformation formula is as follows. 194

3

1

8

1i j

iijij CQGFGECFLPSECSCECI (4) 195

Where CECij is the carbon emission coefficient; 196

ECSij is the energy consumption structure of agricultural products logistics; 197

LPSi is the process structure of agricultural products logistics; 198

ECF is the energy intensity of agricultural products logistics, which is the energy consumption per unit 199

freight volume of agricultural products; 200

FG is the freight intensity of agricultural products logistics, which is the freight volume of agricultural 201

products per unit GDP; 202

CQG is the contribution of the industry, which is the ratio of GDP to the added value of agricultural 203

products logistics industry. 204

CECij, ECF and FG are intensity effect, ECSij and LPSi are structure effect, and CQG is scale effect. 205

Furthermore, the LMDI addition formula is used to decompose the whole process carbon intensity of 206

agricultural products logistics as follows. 207

CQGFGECFLPSECSCEC

TIIIIIIIII 0

(5) 208

Where T is the target year; 209

0 is the base year; 210

IT is the carbon intensity of the target year; 211

I0 is the carbon intensity of the base year; 212

ΔI is the change of carbon intensity of the whole process of agricultural products logistics in the target 213

year compared with the base year; 214

All the differences below represent the carbon intensity changes of the target year relative to the base 215

year. ΔIECS, ΔILPS, ΔIECF, ΔIFG, ΔICQG respectively represents the changes of carbon intensity of the 216

whole process of agricultural products logistics caused by the changes of energy consumption structure, 217

process structure, energy intensity, freight intensity and industrial contribution of agricultural products 218

logistics. ΔICEC is 0, because the carbon emission factor remains unchanged. The corresponding 219

formula is as follows. 220

0

0

000

000

lnlnlnlnln

lnlnln

II

II

I

II

I

II

I

II

I

II

I

II

I

II

T

T

CQG

T

CQG

CQG

FG

T

FGFG

ECF

T

ECFECF

LPS

T

LPSLPS

ECS

T

ECSECS

CEC

T

CECCEC

，，，

，，

(6) 221

3.3 STIRPAT model 222

STIRPAT model is applied to the scenario prediction of carbon intensity of the whole process of 223

agricultural products logistics in China. STIRPAT model is improved from IPAT model (Ehrlich and 224

Holdren 1971). IPAT model is shown in equation (7). 225

TAPI (7) 226

Where I is environmental impact; 227

P is population size; 228

A is affluence; 229

T is technology level. 230

The IPAT model has the defect of elastic unification of influencing factors, but the stochastic 231

STIRPAT model (Dietz and Rosa 1994) established by Dietz et al. overcomes this problem. The 232

STIRPAT model is shown in equation (8), and the natural logarithm is taken on both sides and then 233

transformed into equation (9). 234

eTAPaIdcb (8) 235

eTdAcPbaI lnlnlnlnlnln (9) 236

Where a is the constant term; 237

e is the distractor; 238

b, c and d are the estimated parameters; 239

I, P, A and T have the same meaning as above. 240

The STIRPAT model of carbon intensity of the whole process of agricultural products logistics is 241

constructed as equation (10), so as to predict the carbon intensity of the whole process of agricultural 242

products logistics in China. 243

eTTAAPPaI lnlnlnlnlnlnlnlnln 261524132211 (10) 244

P1, P2, A1, A2, T1 and T2 are the influencing factors of the prediction model of carbon intensity of 245

agricultural products logistics after considering the decomposition factors of LMDI and the actual 246

related effects. 247

Where I is carbon intensity of the whole process of agricultural products logistics; 248

P1 is the urbanization rate; 249

P2 is the freight volume of agricultural products; 250

A1 is GDP per capita; 251

A2 is the added value of farming, forestry, animal husbandry and fishery per unit GDP; 252

T1 is the energy consumption per unit GDP; 253

T2 is the proportion of clean energy in total energy consumption, including natural gas and electricity. 254

P1 and P2 are scale impact, A1 and A2 are economic impact, and T1 and T2 are technological impact. β1, 255

β2, β3, β4, β5 and β6 are the regression coefficients of the above variables, representing the elastic 256

relationship between each influencing factor and carbon intensity of the whole process of agricultural 257

products logistics. Specifically, the carbon intensity of the whole process of agricultural products 258

logistics will change β1%, β2%, β3%, β4%, β5%, and β6% when the influencing factors P1, P2, A1, A2, T1 259

and T2 changed by 1% and the other variables are controlled unchanged. 260

3.4 Quantitative decoupling analysis model 261

Quantitative decoupling analysis model is used to study the decoupling effect between carbon intensity 262

of the whole process of agricultural products logistics and economic development in China. The 263

essential difference between carbon intensity and carbon emission determines the difference of 264

decoupling effect between them and economic development. Aiming at the decoupling effect of carbon 265

intensity and economy, Lu et al. (Lu et al. 2011; Ma and Qin 2021) derived the IGT equation based on 266

the traditional IPAT model, and derived the inequality (11) of the relationship between the decline rate 267

of carbon intensity and economic growth rate based on the absolute quantity decoupling of carbon 268

emission reduction and economic growth. 269

1

12

1 W

WW

(11) 270

1

2

W

WO (12) 271

Where W1 is the growth rate of GDP; 272

W2 is the decline rate of carbon intensity; 273

O is the decoupling index (equation 12). 274

According to the size of W1, W2 and O and the inequality relationship, the decoupling types of 275

economy and carbon intensity are divided into 8 types (Ma and Qin 2021), as shown in table 1. 276

Table 1 State types of quantitative decoupling analysis 277

State I State II W1 W2 Relationship

between W1 and W2

Decoupling

index

Decoupling

Expansive strong

decoupling W1>0 W2>0 W1≤W2 O≥1

Expansive weak decoupling W1>0 W2>0 W1/(W1+1)≤W2<W1 0<O<1

Low-carbon recessive

strong decoupling W1<0 W2>0 W1≤W2 -∞<O<0

High-carbon recessive

strong decoupling W1<0 W2<0 W1≤W2 0<O<1

Recessive weak decoupling W1<0 W2<0 W1/(W1+1)≤W2<W1 1<O<+∞

Negative

decoupling

Expansive negative

decoupling W1>0 W2≤0 W1>W2 -∞<O<0

Coupling Expansive coupling W1>0 W2>0 W2<W1/(W1+1) 0<O<1

Recessive coupling W1<0 W2<0 W2<W1/(W1+1) 0<O<+∞

3.5 Data sources 278

All the index data needed for theoretical research are from China Statistical Yearbook, China Rural 279

Statistical Yearbook, China Energy Statistical Yearbook and China Urban Statistical Yearbook from 280

2000 to 2020. The relevant factors and coefficients for the calculating carbon intensity of the whole 281

process of agricultural products logistics are from the 2006 IPCC Guidelines for National Greenhouse 282

Gas Inventories, GB / T2008 General principles for calculation of total production energy consumption 283

and guidelines for compilation of provincial greenhouse gas inventories. 284

285

4 Results and discussion 286

4.1 Calculation of carbon intensity of the whole process of agricultural products logistics 287

The carbon emission and carbon intensity of the whole process of China's agricultural products 288

logistics are calculated based on the calculation process framework and formula as well as the data of 289

energy and output value from 2000 to 2017. The results are shown in figure (2). The results show that 290

the carbon intensity of the whole process of agricultural products logistics in 2000 was 2.9 tons per 291

10000 yuan, and by 2017, it decreased by about 1.5 tons to 1.4 tons per 10000 yuan. The total carbon 292

emission of agricultural products logistics in China increased from 18.756 million tons in 2000 to 293

55.033 million tons in 2017. The carbon intensity and carbon emissions of the whole process of 294

agricultural products logistics in China showed a fluctuating downward and upward trend respectively 295

from 2000 to 2017. 296

297

Fig. 2 Change trend of carbon emission and carbon intensity of the whole process of agricultural 298

products logistics 299

4.2 Analysis of influencing factors of carbon intensity of the whole process of agricultural products 300

logistics based on LMDI decomposition method 301

The decomposition results of influencing factors of carbon intensity of the whole process of 302

agricultural products logistics from 2000 to 2017 are obtained by introducing the relevant index data 303

into LMDI decomposition formula as shown in Table 2. It is found that the intensity of agricultural 304

products logistics freight transportation (FG) has the greatest impact, which is the most important 305

factor to reduce the carbon intensity in the whole process. The average contribution value is -0.189 T / 306

10000 yuan from 2000 to 2017. The energy consumption structure of agricultural products logistics 307

(ECS) is the secondary influencing factor of carbon intensity reduction in the whole process, and the 308

average contribution value is -0.008 T / 10000 yuan from 2000 to 2017. The energy intensity of 309

agricultural products logistics (ECF) is the main influencing factor of the increase of carbon intensity in 310

the whole process, with an average contribution value of 0.072 tons / 10000 yuan from 2000 to 2017. 311

The contribution of agricultural products logistics to GDP (CQG) is also an influencing factor of 312

carbon intensity, which is not as strong as energy intensity. The average contribution value is 0.038 313

tons / 10000 yuan from 2000 to 2017. The process structure factor of agricultural products logistics 314

(LPS) has little influence on the carbon intensity of the whole process, and the cumulative contribution 315

value is only -0.003 T / 10000 yuan from 2000 to 2017. In addition, intensity effect and structure effect 316

are the primary and secondary factors to reduce carbon intensity of the whole process of agricultural 317

products logistics, while scale effect is the factor to increase carbon intensity of the whole process of 318

agricultural products logistics. They have contributed -1.984, - 0.145 and 0.640 tons / 10000 yuan 319

respectively up to 2017. 320

Table 2 Decomposition results of driving factors of carbon intensity of the whole process of 321

agricultural products logistics from 2000 to 2017 322

Year ΔI ΔICQG ΔIFG ΔIECF ΔILPS ΔIECS Scale

effect

Intensity

effect

Structure

effect

2000-2001 -0.23

6

-0.02

4

-0.21

2 0.003 0.000

-0.00

3 -0.024 -0.209 -0.003

2001-2002 -0.08

4 0.019

-0.13

7 0.040 0.000

-0.00

6 0.019 -0.097 -0.006

2002-2003 0.005 0.183 -0.19

5 0.026

-0.01

3 0.005 0.183 -0.169 -0.009

2003-2004 -0.00

5 0.004

-0.29

2 0.299 0.001

-0.01

7 0.004 0.007 -0.016

2004-2005 -0.09

1 0.022

-0.27

4 0.162

-0.00

1 0.001 0.022 -0.112 0.000

2005-2006 -0.11

3 0.062

-0.28

0 0.109 0.001

-0.00

4 0.062 -0.171 -0.003

2006-2007 -0.25

4 0.052

-0.32

9 0.026 0.004

-0.00

7 0.052 -0.303 -0.004

2007-2008 -0.18

9 0.112

-0.16

3

-0.12

2 0.003

-0.01

9 0.112 -0.285 -0.016

2008-2009 0.058 0.194 -0.29

8 0.152 0.004 0.006 0.194 -0.146 0.010

2009-2010 -0.16

3 0.066

-0.21

5

-0.00

5 0.001

-0.00

9 0.066 -0.220 -0.008

2010-2011 0.047 0.048 -0.33

3 0.336 0.002

-0.00

4 0.048 0.002 -0.003

2011-2012 -0.00

6 0.030

-0.14

6 0.128 0.001

-0.01

9 0.030 -0.019 -0.017

2012-2013 -0.05

3 0.001

-0.12

5 0.076 0.002

-0.00

8 0.001 -0.048 -0.005

2013-2014 -0.14

5

-0.05

2

-0.03

7

-0.04

3 0.000

-0.01

2 -0.052 -0.080 -0.012

2014-2015 -0.03

4

-0.00

8

-0.13

0 0.117 0.001

-0.01

5 -0.008 -0.012 -0.014

2015-2016 -0.09

5

-0.01

9

-0.03

2

-0.03

0

-0.00

2

-0.01

2 -0.019 -0.061 -0.014

2016-2017 -0.13

4

-0.04

8

-0.01

4

-0.04

8

-0.00

4

-0.01

9 -0.048 -0.062 -0.024

Average

contributio

n

-0.08

8 0.038

-0.18

9 0.072 0.000

-0.00

8 0.038 -0.117 -0.009

Cumulative

contributio

n

-1.49

0 0.640

-3.21

1 1.227

-0.00

3

-0.14

2 0.640 -1.984 -0.145

Two conclusions can be drawn by observing the annual cumulative contribution trend of different 323

influencing factors and different types of effects from 2000 to 2017 (Figure 3). First, the increasing 324

trend of energy intensity and the decreasing trend of freight intensity of agricultural products on carbon 325

intensity of the whole process of agricultural products logistics are more and more significant. Second, 326

the decreasing trend of intensity effect and the increasing trend of scale effect on the whole process 327

carbon intensity of agricultural products logistics are more and more significant. 328

329

Fig. 3 Change trend of influencing factors and effects of carbon intensity of the whole process of 330

agricultural products logistics from 2000 to 2017 331

4.3 Scenario analysis and prediction of the carbon intensity of the whole process of agricultural 332

products logistics based on STIRPAT model 333

4.3.1 Analysis of STIRPAT model of the carbon intensity of the whole process of agricultural products 334

logistics 335

In the STIRPAT model of carbon intensity of the whole process of agricultural products logistics, lnI is 336

the predicted variable, lnP1, lnP2, lnA1, lnA2, lnT1 and lnT2 are the explanatory variables. The 337

coefficients are obtained and the model is established by regression analysis. The ADF unit root test is 338

performed on the logarithm of each variable series by Eviews statistical analysis software before 339

regression analysis to determine whether all variables are stationary sequence, so as to prevent the 340

occurrence of pseudo regression. The maximum lag order Pmax is obtained by SIC criterion, the 341

criterion formula (Chen 2014) is shown in equation (13), and T is the sample size. 342

4/1

max 100/12 Tp (13) 343

ADF unit root test results show that all variables are 0-order single integer (Table 3), so the 344

corresponding regression analysis is meaningful. Among them, the sample size T is 18 and the 345

maximum lag order Pmax is 7. 346

Table 3 ADF unit root test results 347

Series ADF

value

Critical value Prob. Is it stable

Stationary

type (c, t, k) 1% level 5% level 10% level

lnI -3.877 -2.792 -1.978 -1.602 0.001 Stable (0,0,0)

lnP1 -41.760 -2.708 -1.963 -1.606 0.000 Stable (0,0,0)

lnP2 -5.554 -4.297 -3.213 -2.748 0.002 Stable (1,0,0)

lnA1 -26.964 -4.297 -3.213 -2.748 0.000 Stable (1,0,0)

lnA2 -6.356 -4.886 -3.829 -3.363 0.001 Stable (1,1,0)

lnT1 -4.460 -4.297 -3.213 -2.748 0.008 Stable (1,0,0)

lnT2 -2.168 -2.728 -1.966 -1.605 0.033 Stable (0,0,0)

Note: c is the constant term, t is the trend term, and k is the difference order. 348

The results of regression analysis by ordinary least squares estimate (OLSE) are as follows (Table 4). It 349

is found that the VIF values (Variance inflation factor) of each variable are greater than 10, which 350

indicates that there is multicollinearity between variables, so this regression analysis method is not 351

applicable to the time series data investigated. 352

Table 4 Results by ordinary least squares estimate 353

Model

Non-standardized

coefficient

Standardized

coefficient t-statistic Sig.

Multiple collinearity

test

B Standard

error Trial version Tolerance

VIF

1

Constant 9.238 6.571 1.406 .187

lnP1 -.430 .737 -.304 -.584 .571 .004 230.545

lnP2 -.493 .563 -.391 -.876 .400 .006 168.874

lnA1 .485 .264 1.562 1.838 .093 .002 612.440

lnA2 .451 .294 .413 1.537 .153 .016 61.297

lnT1 .600 .201 .934 2.981 .012 .012 83.209

lnT2 -.551 .294 -.533 -1.876 .087 .015 68.352

a. dependent variable \: lnI

Ridge regression method is used to fit the model to overcome the multicollinearity problem. This 354

method reduces the variance of parameter estimator and improves the stability of estimation by 355

introducing nonnegative factor (error) and losing part of information (Hoerl and Kennard 2000), in 356

which the ridge parameter k is as small as possible to reduce data loss. SPSS and SPSSAU statistical 357

analysis software were used for ridge regression analysis of the logarithm of the variable series. The 358

fitting result of ridge regression tends to be stable and the fitting effect is good when k is 0.14 359

according to the ridge trace diagram (Table 5). 360

Table 5 Results by ridge regression 361

Non-standardized

coefficient

Standardized

coefficient t p R2

Adjust

R2 F

B Standar

d error Beta

Constant 2.86 0.69 - 4.145 0.002**

0.973 0.959

F

(6,11)=

66.458,

p

=0.000

lnP1 -0.191 0.035 -0.135 -5.504 0.000**

lnP2 -0.116 0.051 -0.092 -2.256 0.045*

lnA1 -0.04 0.005 -0.129 -7.381 0.000**

lnA2 0.203 0.044 0.186 4.616 0.001**

lnT1 0.142 0.023 0.221 6.301 0.000**

lnT2 -0.216 0.055 -0.209 -3.931 0.002**

dependent variable: lnI

* p<0.05 ** p<0.01

Note: t statistics in parentheses, and *p < 0.05, **p < 0.01, respectively, indicating significant levels of 362

1% and 5%, the same below. 363

The R2 value of the model is 0.973, which indicates that lnP1, lnP2, lnA1, lnA2, lnT1 and lnT2 can 364

explain 97.32% of the change of lnI. The overall P value of the model is 0.000, less than 0.05, which 365

indicates that the model fitting is meaningful. In F test, F is 66.458, P is 0.000, less than 0.05, which 366

indicates that the model passes F test, at least one of the independent variables will affect the dependent 367

variable, and the model is determined as follows. 368

212

121

ln216.0ln142.0ln203.0

ln040.0ln116.0ln191.0860.2ln

TTA

APPI

(14) 369

The results showed that lnA2 and lnT1 had significant positive effects on lnI, while lnP1, lnP2, lnA1 and 370

lnT2 had significant negative effects on lnI, and the influence degree of different variables was different. 371

All the coefficients are in line with the economic significance. Every 1% change of urbanization rate, 372

freight volume of agricultural products logistics, per capita GDP, the added value of farming, forestry, 373

animal husbandry and fishery per unit GDP, energy consumption per unit GDP and the proportion of 374

clean energy in unit energy consumption will lead to changes of - 0.191%, - 0.116%, - 0.04%, 0.203%, 375

0.142% and - 0.216% of carbon intensity of the whole process of agricultural products logistics. 376

4.3.2 Scenario prediction of carbon intensity of the whole process of agricultural products logistics 377

The STIRPAT model is used to predict the carbon intensity of the whole process of China's agricultural 378

products logistics in the future. The variables in the prediction model are divided into economic 379

development indexes and emission reduction indexes. The economic development indexes include lnP1, 380

lnP2, lnA1 and lnA2, and the emission reduction indexes include lnT1 and lnT2. The two types of 381

indexes are set as high, medium and low growth (or reduction) rates respectively, and then nine 382

scenarios are combined. We set the corresponding change rate in the scenario by referring to the 383

existing research literature (Zhang and Su 2020) and the actual change trend of the variables related to 384

the carbon intensity of agricultural products logistics in China from 2000 to 2017, and forecast the 385

carbon intensity of the whole process of agricultural products logistics in China from 2018 to 2030 386

under different circumstances by using SPSS (Table 6). 387

Table 6 Scenario design and growth rate setting of carbon intensity of the whole process of agricultural 388

products logistics 389

Scenario

design

Rate of

economic

development

Economic development indexes Rate of

emission

reduction

Emission reduction

indexes

P1 P2 A1 A2 T1 T2

Scenario

1 High 1.6% 4.1% 6.0% -5.7% High -7.6% 1.5%

Scenario

2 High 1.6% 4.1% 6.0% -5.7% Medium -5.6% 1.1%

Scenario

3 High 1.6% 4.1% 6.0% -5.7% Low -3.6% 0.7%

Scenario

4 Medium 1.2% 3.1% 4.4% -3.7% High -7.6% 1.5%

Scenario

5 Medium 1.2% 3.1% 4.4% -3.7% Medium -5.6% 1.1%

Scenario

6 Medium 1.2% 3.1% 4.4% -3.7% Low -3.6% 0.7%

Scenario

7 Low 0.8% 2.1% 2.8% -1.7% High -7.6% 1.5%

Scenario

8 Low 0.8% 2.1% 2.8% -1.7% Medium -5.6% 1.1%

Scenario

9 Low 0.8% 2.1% 2.8% -1.7% Low -3.6% 0.7%

The results show that the scenario of high economic growth and high emission reduction rate has the 390

fastest downward trend of carbon intensity, which is scenario 1, while the scenario of low economic 391

growth and low emission reduction rate has the slowest downward trend, which is scenario 9. The 392

control targets of carbon intensity in 2020 and 2030 are 40% - 45% and 60% - 65% lower than that in 393

2005, respectively. Only scenario 1 and scenario 2 jointly achieve the carbon intensity control targets of 394

China in 2020 and 2030. The scenario design of high economic growth and high emission reduction 395

rate will reduce the carbon intensity of the whole process of agricultural products logistics by 45.5% 396

and 62.3% in 2020 and 2030 respectively, and the scenario design of high economic growth and 397

medium emission reduction rate will reduce the carbon intensity by 44.7% and 60.3% respectively. 398

China's carbon intensity is about 48.4% lower than that of 2005 by the end of 2020 in fact, which also 399

confirms the accuracy of the model. The design of scenarios 3-9 can achieve the carbon intensity 400

control target in 2020, but it cannot ensure the realization of the target in 2030. Scenario 3-5 will 401

decrease by 55% - 60% in 2030, and scenario 6-9 will decrease by 50% - 55% in 2030 (Figure 4). 402

403

Fig. 4 Change trend of carbon intensity of the whole process of agricultural products logistics under 404

different scenarios 405

4.4 Decoupling effect analysis of carbon intensity and economic development of the whole process of 406

agricultural products logistics based on quantitative decoupling analysis model 407

The economic growth rate W1, carbon intensity reduction rate W2 and decoupling index O are obtained 408

by using China's GDP and the carbon intensity data of the whole process of agricultural products 409

logistics from 2000 to 2017. The decoupling status and trend of carbon intensity of the whole process 410

of agricultural products logistics and economic development in China are analyzed. The results are as 411

follows (Table 7). 412

Table 7 Decoupling status of carbon intensity of the whole process of agricultural products logistics 413

and China's economic development 414

Year W1 W2 O State I State II

2001 0.106 0.081 0.770 Coupling Expansive coupling


2003 0.129 -0.002 -0.015 Negative decoupling Expansive negative

decoupling







decoupling



decoupling



2014 0.085 0.079 0.930 Decoupling Expansive weak decoupling




The results show that the agricultural products logistics carbon intensity in the whole process and 415

economic development in China are in expansive negative decoupling state in 2003, 2009 and 2011, in 416

expansive weak decoupling state in 2014, and in expansive coupling state in the rest years from 2001 to 417

2017. Specifically, the relationship between the carbon intensity of the whole process of agricultural 418

products logistics and the economy is in a state of expansive negative decoupling in 2003, 2009 and 419

2011, which means that China's economy is in a high-speed growth, and the corresponding the carbon 420

emissions of the whole process of agricultural products logistics also continue to grow. In other words, 421

the rapid economic growth is at the cost of high carbon emissions of agricultural products logistics. The 422

carbon intensity of the whole process of agricultural products logistics and economic growth is in 423

expansive weak decoupling state in 2004. It shows that the economic growth in China has been 424

accompanied by a small decrease in the carbon intensity of the whole process of agricultural product 425

logistics, but the decline rate of carbon intensity is lower than that of economy. In addition, the 426

agricultural products logistics carbon intensity in the whole process and economic growth is in the 427

expansive coupling state in the rest years, which indicates that China's economy is developing rapidly 428

most of the time, and the carbon intensity is also declining, but the economic growth rate is greater than 429

the carbon intensity decline rate. Furthermore, the decoupling index of expansive coupling is on the 430

rise, which indicates that the decoupling state between carbon intensity of agricultural products 431

logistics and economic development in China is gradually developing towards expansive strong 432

decoupling. It shows that the decline rate of carbon intensity of agricultural products logistics in China 433

is gradually greater than the economic growth rate, the economic development is in a good state, and 434

the carbon intensity of agricultural products logistics is about to be more effectively controlled. 435

436

5 Conclusions and policy implications 437

The research on the influencing factors, scenario prediction and decoupling effect of carbon intensity of 438

the whole process of agricultural products logistics is of great significance for comprehensively and 439

systematically reducing the carbon intensity of agricultural products logistics in China and achieving 440

the carbon intensity control target in 2030. This study combined LMDI decomposition method, 441

STIRPAT model and quantitative decoupling analysis model to carry out theoretical discussion, which 442

provides theoretical basis for the control of the overall carbon intensity of agricultural products 443

logistics. The LMDI decomposition method is used to decompose the influencing factors of carbon 444

intensity of the whole process of agricultural products logistics from 2000 to 2017. The STIRPAT 445

model is established to predict the carbon intensity of the whole process of agricultural products 446

logistics in nine different scenarios from 2018 to 2030. The quantitative decoupling analysis model is 447

applied to analyze the decoupling effect and trend between carbon intensity of the whole process of 448

agricultural products logistics and China's economic development from 2000 to 2017. 449

5.1Conclusion 450

The specific conclusions are as follows. 451

Firstly, the freight intensity and energy consumption structure of agricultural products logistics are the 452

primary and secondary influencing factors of carbon intensity reduction of the whole process of 453

agricultural products logistics. The energy intensity and industrial contribution of agricultural products 454

logistics are the primary and secondary influencing factors of the increase of carbon intensity in the 455

whole process. The logistics process structure has little influence on the reduction of carbon intensity 456

of the whole process of agricultural product logistics. In addition, intensity effect and structure effect 457

are the primary and secondary factors to reduce carbon intensity of the whole process of agricultural 458

products logistics, while scale effect is the main factor to increase carbon intensity. 459

Secondly, the scenario of high economic growth and high emission reduction rate leads to the fastest 460

decline of carbon intensity of the whole process of agricultural products logistics. The scenario of low 461

economic growth and low emission reduction rate leads to the slowest decline of carbon intensity of the 462

whole process of agricultural products logistics. The scenarios of high economic growth, high emission 463

reduction rate and high economic growth, medium emission reduction rate can achieve China's carbon 464

intensity control goals in 2020 and 2030 at the same time. The remaining scenarios can only achieve 465

the carbon intensity control goals in 2020, but cannot achieve the carbon intensity control goals in 466

2030. 467

Thirdly, there are 13 years of expansive coupling, 3 years of expansive negative decoupling and 1 year 468

of expansive weak decoupling between carbon intensity of the whole process of agricultural products 469

logistics and economic development in China. On the whole, with the rapid development of China's 470

economy, carbon intensity of the whole process of agricultural products logistics is also declining, but 471

the rate is lower than the economic growth rate. The decoupling index of the expansive coupling state 472

is on the rise, and gradually develops to the expansive strong decoupling state, which indicates that 473

China's economic development is in a good state, and carbon intensity of the whole process of 474

agricultural products logistics is about to be more effectively controlled. 475

5.2 Policy implications 476

The policy recommendations to reduce carbon intensity of the whole process of agricultural products 477

logistics in China according to the above research conclusions are as follows. 478

Firstly, increase the freight volume of agricultural products per unit of GDP and reduce the energy 479

consumption of the freight volume of agricultural products, so as to enhance the intensity effect. 480

Optimize the energy consumption structure and process structure of agricultural products logistics, 481

increase the proportion of clean energy, promote the coordinated low-carbon development of the 482

transportation, storage and packaging process, so as to enhance the structural effect. Improve the added 483

value of agricultural products logistics, improve the contribution of agricultural products logistics 484

industry to GDP, so as to reduce the scale effect. 485

Secondly, increase the urbanization level, the freight volume of agricultural products, the growth rate 486

of per capita GDP and the reduction rate of the added value of farming, forestry, animal husbandry and 487

fishery per unit GDP. Maintain or increase the reduction rate of energy consumption per unit GDP and 488

the growth rate of clean energy ratio in energy consumption per unit GDP. Build the development 489

scenarios of high economic growth, high emission reduction rate and high economic growth, medium 490

emission reduction rate of the whole process of China's agricultural products logistics. 491

Thirdly, increase the economic growth rate and the carbon intensity reduction rate of the whole process 492

of agricultural products logistics on the basis of maintaining the existing carbon emission reduction 493

results of the whole process of agricultural products logistics. Speed up the economic development and 494

reduce the carbon emission of the whole process of agricultural products logistics, So as to realize the 495

decoupling of carbon intensity of the whole process of agricultural products logistics and economic 496

development, and then promote carbon intensity of the whole process of agricultural products logistics 497

to be control effectively and efficiently. 498

499

Declarations 500

501

Ethical Approval 502

Not applicable 503

Consent to Participate 504

Not applicable 505

Consent to Publish 506

Not applicable 507

508

Author contributions 509

All authors contributed to the study conception and design. Material preparation was performed by 510

Xueru Fan, Guanxin Yao, Dongmei Zhang, and Xiaoyu Bian; data collection was performed by Xueru 511

Fan and Dongmei Zhang; and empirical analysis was performed by Xueru Fan, Guanxin Yao, and 512

Xiaoyu Bian. The first draft of the manuscript was written by Xueru Fan, and all authors commented 513

on previous versions of the manuscript. All authors read and approved the final manuscript. 514

515

Funding 516

This work was supported by the following projects: the National Natural Science Foundation of China 517

(no.71773104), the China Postdoctoral Science Foundation (no.2019M661960), the Key Project of 518

Philosophy and Social Science in Jiangsu Province (no.20GLA002), and the Project of Jiangsu 519

Decision Making Consulting Research Base (no.20SSL118). At the same time, all the authors would 520

like to express their sincere thanks to the editors for their work and the reviewers for their suggestions. 521

522

Data availability 523

The datasets used and/or analyzed during the current study are available from the corresponding 524

statistical yearbook of China. 525

526

Compliance with ethical standards 527

Competing interests 528

The authors declare that they have no competing interests 529

530

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