Industrial sector is one of the indispensable contributors in global warming. Even if the occurrence of ecoindustrial parks (EIPs) seems to be a good improvement in saving ecological crises, there is still a lack of definitional clarity and in-depth researches on low-carbon industrial parks. In order to reveal the processes of carbon metabolism in a low-carbon high-tech industrial park, we selected Beijing Development Area (BDA) International Business Park in Beijing, China as case study, establishing a seven-compartment- model low-carbon metabolic network based on the methodology of Ecological Network Analysis (ENA). Integrating the Network Utility Analysis (NUA), Network Control Analysis (NCA), and system-wide indicators, we compartmentalized system sectors into ecological structure and analyzed dependence and control degree based on carbon metabolism. The results suggest that indirect flows reveal more mutuality and exploitation relation between system compartments and they are prone to positive sides for the stability of the whole system. The ecological structure develops well as an approximate pyramidal structure, and the carbon metabolism of BDA proves self-mutualistic and sustainable. Construction and waste management were found to be two active sectors impacting carbon metabolism, which was mainly regulated by internal and external environment.
The study of ecoindustrial parks (EIPs) has assumed great deal of importance within the past ten to fifteen years. One of the best definitions of an EIP has been provided by the UESPA, which was stated as “
Despite cooperation between companies to find win-win solutions [
Climate change announces a fast global socioeconomic transition, but nobody could predict the ultimate results of the next industrial revolution. Obviously, business and government play a key role in promoting or destroying the revolution. It is undeniable that climate change affects both natural ecosystems and human societies [
An industrial ecosystem is constructed with the flows of matter, nutrients, energy, and carbon [
EIPs originate from three major concepts, namely, scilicet industrial ecology, biological ecology, and the spatial perspectives based on landscape ecology [
Nowadays, diverse analysis tools are used in the research of EIPs, which can be classified into two main trends of methodology. One of them is generally based on the inventorying of life-cycle ecological and economic input-output flows, including material flow analysis [
Ecological Network Analysis (ENA) is a general metabolism-based analytical tool for studying the system connectivity and for quantifying and qualifying direct and indirect ecological flows in the system [
In China, there exists a conflict among economic development, shortage of natural resource, and serious pollution suggested by the impetus for developing EIPs [
The essence of ecological network model is a transmitting network for materials and energy, which includes both of the components’ mutual interactions in integral environment and the passing relationship between integral and external environment. For this sake, establishing the reasonable system boundary and making sure of limiting factors should be the necessary step for the ecological network model.
Even though EIPs are mainly artificially controlled, both artificial and natural processes of parks’ carbon fluxes should be taken into account in the network model. The system boundary does not just coincide with the administrative boundaries, but a virtual boundary that contains metabolic processes links both inside and outside of the park. Taking carbon metabolic processes and their relationship through different compartments within the virtual boundary, a metabolic network model for low-carbon high-tech industrial parks (we might call it as Low-Carbon Metabolic Network (LCMN) as well) is established for tracking carbon flows within an low-carbon park ecosystem (Figure
Metabolic network model for carbon metabolism of BDA International Business Park. The matrix at the right bottom is the adjacency matrix A of the model, where
“Utility” is an economic conception similar to “efficiency.” Since Patten [
In NUA, Sign
Fath and Patten [
Patten [
Be similar as the direct utility matrix
For the output environ, from the generating or flow-forward transfer efficiencies and the receiving or flow-backward transfer efficiencies
Based on the network control and dependence formulation, the system-wide control condition can be revealed by the system control index (CI). CI combines control degree with dependence degree, and thus it indicates the control utility and organization capability of the whole system and can be employed to index the self-regulation of system metabolism [
For the purpose of giving an overall perspective on metabolic performance of industrial park and contributing to design a both sustainable and low-carbonic park, it is necessary to define a set of indicators in addressing the system performance of the MN. Some of these indicators have already been introduced by NUA and NCA as above, while others were extracted from other researches of ENA [
System-wide indicators of metabolism model.
Indicators | Formulation | Short description |
---|---|---|
Nodes | m | Quantity of metabolic compartments, also the size of network |
Links | L | Quantity of metabolic direct flows or arcs |
Link density | L/m | Metabolic linking degree |
Connectance | L/m2 | Metabolic connectivity, also the proportion of realized direct pathways |
MI | Equation ( |
Metabolic system |
SI | Equation ( |
Metabolic system synergism |
CI | Equation ( |
Self-regulation of metabolism |
The metabolism data sources were extracted from construction and operation data of BDA which were all calculated based on the IPCC recommended method and life cycle analysis. These data originated from investigations and calculation into carbon composition of artificial activities, raw materials’ transportation, the relationships between these flows and stocks, and also within anthropogenic-natural processes.
Carbon fluxes between two compartments within LCMN of BDA are listed in Table
Carbon flows within the low-carbon metabolic network of BDA (unit: t CO2-eq)a.
Doner/accepter | Int | Eng | Con | IBS | Wst | Grn | Ext |
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Int | 0 | 3046 | 0 | 39 | 0 | 0 | 6014 | 9100 |
(0.0%) | (1.4%) | (0.0%) | (0.0%) | (0.0%) | (0.0%) | (2.8%) | (4.3%) | |
Eng | 0 | 0 | 0 | 0 | 0 | 0 | 12796 | 12796 |
(0.0%) | (0.0%) | (0.0%) | (0.0%) | (0.0%) | (0.0%) | (6.0%) | (6.0%) | |
Con | 13 | 0 | 0 | 6001 | 0 | 0 | 82509 | 88523 |
(0.0%) | (0.0%) | (0.0%) | (2.8%) | (0.0%) | (0.0%) | (38.6%) | (41.4%) | |
IBS | 6001 | 8288 | 0 | 0 | 0 | 0 | 23 | 14312 |
(2.8%) | (3.9%) | (0.0%) | (0.0%) | (0.0%) | (0.0%) | (0.0%) | (6.7%) | |
Wst | 0 | 0 | 17547 | 0 | 0 | 0 | 0 | 17547 |
(0.0%) | (0.0%) | (8.2%) | (0.0%) | (0.0%) | (0.0%) | (0.0%) | (8.2%) | |
Grn | 2176 | 4644 | 6408 | 2234 | 0 | 0 | 36920 | 52382 |
(1.0%) | (2.2%) | (3.0%) | (1.0%) | (0.0%) | (0.0%) | (17.3%) | (24.5%) | |
Ext | 0 | 1462 | 0 | 0 | 17547 | 0 | 0 | 19009 |
(0.0%) | (0.7%) | (0.0%) | (0.0%) | (8.2%) | (0.0%) | (0.0%) | (8.9%) | |
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8190 | 17441 | 23955 | 8274 | 17547 | 0 | 138262 |
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(3.8%) | (8.2%) | (11.2%) | (3.9%) | (8.2%) | (0.0%) | (64.7%) |
aThe numbers in parentheses mean the proportion of carbon flows, namely, the flow value divided by the total carbon flow in the whole system.
The proportion of carbon flows within each sector can reflect the ecological structure of the carbon metabolism in BDA, which forms an approximate pyramidal shape (Figure
Ecological structure of carbon metabolism in BDA.
Reading from left to right, the values are the total carbon inputs or outputs (Grn fits the output value) of compartments. Producer: supplying distal carbon into BDA system; decomposer: releasing carbon for producers’ reuse by decomposition of the waste; first consumer: carbon agents that transfer carbon from natural environment to human society; second consumer: anthropogenic using processed carbon resources from first consumers by processes of creating products or utilizing energy; sink: eliminating carbon through photosynthesis of green trees, it does not belong to the trophic structure.
Table
Direct utility sign matrix (
Int | Eng | Con | IBS | Wst | Ext | |
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Con |
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IBS |
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aDue to the value of total supplied carbon from Grn is zero, as the carbon sink, we can consider that the mutual relationships between other compartments and Grn are exploit-exploited (including Int, Eng, Con, IBS, and Ext) or neutrality (including Wst). In this sense, we did not take into consideration Grn calculation and comparison in Table
Then, we tried to analyze each trophic level. There are some differences in respective mutual relations between producers (Int and Ext). Int has a variety of links and its direct and integral relationships are not unified. Both the direct and indirect linkages for Ext mainly indicate that it exploits consumers (Eng, Con, and IBS) in terms of advancing raw materials, apparatus, and energy, and then being exploited by decomposer (Wst) for carbon recycle. As the largest carbon doners, Ext dominates the contacts between producers and other compartments. The irreplaceable effect of decomposer leads to obvious direct relationships that Wst is exploited by first consumer (Con) in dealing with carbon waste and pays back to producer (Ext). Besides, there are no more direct links between other consumers and producer but diverse indirect interactions. First consumers (Eng and Con) play a significant role in transform carbon from distal environment into industrial society, so they mainly exchange carbon with producer (especially Ext) in terms of raw materials and energy. And particularly, Con is exploited by producer and second consumer (IBS) in material import and service management. Located at the highest trophic level, utility consumer (IBS) is mainly exploited by producer (Int and Ext) and first consumers (Eng and Con), for it acts better as a carbon recipient. Yet the direct and integral relationships between IBS and the decomposer (Wst) are in neutrality and mutualistic condition. That is because there is no direct carbon exchange between them, while both of them are exploited by Con as a carbon accepter in indirect ways. In a word, these compartments all play their own role in ecological structure of carbon metabolism in BDA.
Figures
(a) Network control of carbon metabolism in BDA. (b) Network dependence of carbon metabolism in BDA. Because the value of total supplied carbon from Grn is zero, we did not consider the influence from Grn in these figures for an easy calculation.
From the dependent perspective, Eng and Con as first consumers have some differences on dependence degree, and there exists an intrinsic linkage. Eng is more controlled by Con (60.4%), while Con is dependent on second consumer’s management (IBS, 47.6%). The second consumer (IBS) generally depends on Eng’s energy furnish (32.5%), whereas the influence from Wst (6.3%) is too small to mention. And at the decomposer’s level (Wst), the control degree is mostly contributed by first consumers, where Con achieves 47.6% and Eng as 31.7%. The control condition shows a similarity (even more pronounced) in the systemic control for carbon metabolic in the views of controller. In addition, by observing the producers dependence degree, first consumers are more dominated by Ext (Eng is 19.4%, while Con is 52.4%), while second consumer (IBS, 61.0%) and decomposer (Wst, 20.7%) are more controlled by Int. Similar results have also been reflected in the control condition, where the control proportions from Ext to IBS and Wst reach 46.9% and 94.1%, while those from Int to Eng and Con achieve 12.7% and 2.4%. These similar results announce that the BDA International Park is further regulated by the outside world (especially by the external environ), so as to enhance the integral cooperation between those compartments, which may be a promising way to improve the utility of carbon metabolic in BDA.
The calculating values of system-wide indicators for carbon metabolism in BDA are all shown in Table
System-wide indicators of carbon metabolism in BDA.
Indicators | Formulation | Low-carbon metabolic network | |
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with Int and Ext | without Int and Ext | ||
Nodes | m | 7 | 5 |
Links | L | 15 | 6 |
Link density | L/m | 2.14 | 1.20 |
Connectance | L/m2 | 0.31 | 0.24 |
MI | Equation ( |
1.50 | 1.40 |
SI | Equation ( |
3.57 | 2.54 |
Based on the methodology of ENA, we established a metabolic model for low-carbon high-tech industrial parks and analyzed the carbon metabolic system of the BDA International Business Park in this research. The results reflected the behaviors and potential linkage of system compartments and revealed the structure, function, and mutualism condition of the carbon metabolic system in the case park. In the relationship and control analyses, compartments links are quite diverse and positive, especially the Con and Wst, which play a pivotal role in exchanging carbon flows between industrial compartments and environs (both Int and Ext). Comparing the scenarios with Int and Ext or not, variations of system-wide indicators have revealed the significance of artificial control including carbon supplying and decomposing. Regarding ecological structure and function, the pyramidal ecological structure and positive indicators (MI and SI) both show a system mutualism and sustainable condition, which makes BDA Park a demonstration project in Chinese low-carbon EIPs’ construction. Yet if we enhanced the effect of waste management part, different system functions would go on better.
This study was supported by Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 51121003), Key Program of National Natural Science Foundation (NOs. 50939001, 41271543), and Program for New Century Excellent Talents in University (NCET-09-0226). We are especially grateful for the financial support from Beijing Development Area Co. Ltd.