International Urology and Nephrology

, Volume 49, Issue 2, pp 337–343 | Cite as

The carbon footprints of home and in-center peritoneal dialysis in China

  • Mindong Chen
  • Rong Zhou
  • Chongbo Du
  • Fulei Meng
  • Yanli Wang
  • Liping Wu
  • Fang Wang
  • Yahong Xu
  • Xiufen Yang
Nephrology - Original Paper

Abstract

Objective

The provision of healthcare itself is associated with abundant greenhouse gas (GHG) emissions. This study aims to determine the carbon footprints of peritoneal dialysis (PD) with the different modalities and treatment regimes.

Methods

A total of 68 subjects performed with PD treatment were enrolled in this study. Emissions factors were applied to data that were collected for energy consumption, travel, and procurement.

Results

The carbon footprints generated by the provision of PD treatment for the individual patient were calculated and normalized to a 2-l PD dialysate volume. The fixed emissions were higher in patients who received PD therapy in center than at home, mostly attributing to the consumption of electricity. Conversely, PD treatment performed in center yielded less variable emissions than that of at home, which resulted from reduced constituent percentage of waste disposal and transportation. Collectively, packaging consumption mostly contributed to the total carbon footprints of PD.

Conclusion

This study for the first time demonstrates the delivery of PD is associated with considerable GHG emissions, which is mainly attributed to packaging materials, transportation, electricity, and waste disposal. These results suggest that carbon reduction strategies focusing on packaging consumption in PD treatment are likely to yield the greatest benefits.

Keywords

Greenhouse gas (GHG) emissions Carbon footprints Peritoneal dialysis 

Introduction

Climate change is a major global health threat driven by greenhouse gas (GHG) emissions [1]. Similar to other developing countries, China is experiencing intense air pollution caused in large part by anthropogenic emissions of GHG [2]. As a common human activity, the provision of healthcare itself is associated with abundant GHG emissions, although public health might receive significant benefits from GHG mitigation [3]. Recent findings reveal that healthcare service might be responsible for significant proportions of total carbon footprint in developed countries [4, 5]. Thus, the healthcare sector can play a fundamental role in primary GHG mitigation by reducing its environmental footprint. It is necessary for worldwide healthcare institutions to better understand the environmental footprint of their operations and services, particularly for those power- and water-greedy therapies.

Renal replacement therapy (RRT) is a life-sustaining treatment for patients with end-stage renal disease (ESRD), which includes hemodialysis (HD) and peritoneal dialysis (PD). As the most widely applied RRT, conventional HD is an especially power and water-hungry therapy, which continuously raises environmental concerns due to the rising clinical practice [6]. Moreover, the anticipated rise in the prevalence of home HD patients, dialyzing more frequently and for longer than in-center patients, will increase the GHG emissions associated with HD programs [7]. Although the exact epidemiological data remain lacking, it is currently estimated that the world HD service would annually consume 1.62 billion kWh of power and generate about 625,000 tons of plastic waste [8]. Thus, the environmental impact of HD should be critically considered and improved in the future RRT practice.

PD is an effective alternative form of RRT that is currently used by approximately 11% of the total global dialysis population [9]. PD holds extensive therapeutic advantages for appropriately selected patients when compared with HD, including early survival benefit, retaining residual kidney function, lower infection risk, and convenience of home therapy [10, 11]. Importantly, the healthcare costs of PD in most countries are much lower than those of in-center HD [12]. For example, the annual cost of PD in China is about 17% less expensive than that of in-center HD [11]. This economic advantage of PD is even more prominent in developed countries, such as UK [13] and Canada [14]. Given the cost-efficacy advantage of PD, it is intriguing to address whether PD might benefit GHG mitigation as well.

This study is aimed to for the first time determine the carbon footprints of the differing modalities and treatment regimes used to deliver PD. The results represent the first assessment on the carbon footprint of PD patients in the Chinese population, which might improve our understanding of PD-associated GHG emissions and facilitate carbon reduction strategies at the level of both PD treatments and RRT programs.

Materials and methods

Patients

PD patients at our medical center between January 2010 and December 2014 were eligible for this study. All patients gave informed consent before inclusion. The study protocol was reviewed and approved by the Human Research Ethics Committees in the Tongji University School of Medicine. On admission to the hospital, demographic and clinical data of individual subject were collected. Inclusion criteria were listed as follows: stable on PD for at least 6 months, peritonitis-free for at least 3 months, ability to understand the nature, and requirements of the clinical investigation. Patients were excluded from the study if any one of the following conditions was present: dialysis for acute kidney injury, severe heart failure, liver disease, malignant tumor, and life expectancy less than 6 months. A total of 68 PD patients were enrolled. PD patients were treated with either continuous ambulatory peritoneal dialysis (CAPD) or daytime ambulatory peritoneal dialysis (DAPD). The daytime exchange consisted of 1.5 or 2.5% glucose with total volume of 6–8 l per day in all patients. Automated PD was not included in this study due to the limited numbers of subjects.

Data collection

The methodology applied to calculate the total GHG emissions for all PD therapy was based on the protocol of PAS 2050, which has been developed by the British Standards Institution (BSI) and Department for Environment Food and Rural Affairs (DEFRA) [15]. Activity data were collected for energy and water use, patient and staff travel, paper, electricity, waste disposal to landfill, waste recycling and procurement associated with the provision of PD treatment. These data were converted to a common measurement unit of tCO2-eq using standard derived emissions factors [15], which have also been performed in previous GHG studies of HD [7]. The emissions factors are described further under specific subheadings in this section.

GHG emissions terminology and unit of measurement

Although multiple gases with global warming potential are suggested by the Kyoto Protocol [16], only three are commonly used [carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O)]. CO2 is most commonly used as the reference gas, with the emissions of the other gases being expressed in the units of CO2 equivalents (CO2eq). This study reports the total emissions in kg of CO2eq per year. The GHG emissions of PD treatment consist of fixed, variable, and random variable emissions, which are normalized to a 2-l PD dialysate dose and presented as median (percentage).

Measurement of PD emissions factors

The sources of emissions included in PD treatment were identified as individual components of providing repeated treatment sessions and the impact of these components in terms of building energy use, travel, transport, procurement, and waste. PD treatment was strictly performed for each individual subject with a fixed set of procedure. We calculated the emission factors of each part of the PD dialysate solution and packaging materials, according to Inventory of Carbon and Energy database [17]. According to PD practice, dialysate packaging materials were the major sources of PD-associated carbon emissions, including PD solution bags (polyvinyl chloride, PL-146), individual plastic bags, and external packaging boxes. Additional energy use for product transportation was not able to calculate. Therefore, we set up an additional 10% of the emission factors in line with the previous study [7].

Travel and transport

In this study, ‘travel’ is defined as the movement of people and ‘transport’ as the movement of products [15]. All data were derived from patients travel, including outpatient appointments, inpatient admissions, dialysis treatments, and laboratory investigations. All data collected included the distances and modalities of travel (active travel, car, bus, train, and air) according to the travel invoice records.

Electricity consumption

PD-related energy consumption included dialysate heating and disinfection. For in-center PD patients, data were recorded for dialysate incubator (SPX-250, 350Wh) and ultraviolet light disinfection. Other home PD patients used dialysate heating pack (SE-4399N, 60Wh) and ultraviolet light disinfection as well. In China, thermoelectric power (mainly coal power) accounted for 78% of electricity generation. Carbon emission factor of coal power was calculated as 0.92 kg CO2 per kilowatt [18].

Water consumption

All patients received standard PD therapy with commercial dialysate of 2-l volume. For DAPD patients, an additional bag of dialysate was used as the empty bag to drained peritoneal fluid.

Hand washing

Hand washing is a standard procedure prior to PD operation. Average water consumption for each hand washing was 4 l with routine use of two pieces of disposable paper towels to dry.

Office supplies

Standard paper notebooks (0.13 kg) were used for daily dialysis records of home PD. In center, PD data were mostly inputted into computers with supplemented paper record. Waste paper was not calculated in this study.

Waste

PD produces clinical and domestic waste. The former underwent incineration, while the latter went primarily to landfill or recycling. As the practice of medical waste management varies, we assumed that optimal waste management strategies were implemented within reasonable and practical limits. Primary activity data, in the form of the weight and constituent materials of the waste produced by each PD regime, have been identified by direct measurement, to which DEFRA emissions factors have been applied [15, 19]. For home PD patients, 50% of dialysate packages were assumed to be recycled while other 50% went to incineration. For in-center PD, all packages were recycled. All plastic packages from domestic waste went to landfill. For in-center waste, exterior bags of dialysate went to landfill and interior bags were incinerated.

Emissions factors

The emissions factors used in the calculation of the carbon footprints of the different forms of PD are shown in the Tables 1, 2, and 3, which have derived from DEFRA reports [15] and Inventory of Carbon and Energy database [17]. The carbon footprints generated by the provision of PD treatment for an individual patient were calculated and normalized to a 2-l PD dialysate volume.
Table 1

Emissions factors for patient and visitor travel, freight transport and energy data

Source

Category

Emissions factor for conversion to GHG emissions (kgCO2Eq/km)

Transportation

Active travel (walking/bicycle)

0

Car

0.20487

Taxi

0.15965

Shuttle bus

0.10462

Subway

0.06113

Ambulance

0.26866

Hospital vehicle (multiple passengers)

0.22608

Freight wagon

0.80201

Energy

Electricity

0.92

Table 2

Emissions factors for PD performance

Source

Category

Emissions factor for conversion to GHG emissions

Medical supplies

General plastics

2.53 kgCO2/kg

 

Low-density polyethylene

1.7 kgCO2/kg

 

High-density polyethylene

1.6 kgCO2/kg

 

General polyethylene

1.94 kgCO2/kg

 

Polypropylene

2.7 kgCO2/kg

 

Polyurethane

3 kgCO2/kg

 

Polycarbonate

6 kgCO2/kg

 

Polyvinyl chloride

2.41 kgCO2/kg

 

Paper

1.5 kgCO2/kg

 

Stainless steel

6.15 kgCO2/kg

 

Rubber

3.18 kgCO2/kg

 

Copper

3.83 kgCO2/kg

 

Activated carbon

(polymer coated)

0.67 kgCO2/kg

 

Cardboard

1.32 kgCO2/kg

 

Pharmaceuticals

0.8 kgCO2/₤

Office supplies

Plastic products

1.13 kgCO2Eq/₤

 

Metal products

1.18 kgCO2Eq/₤₤

 

Office machinery and computers

0.58 kgCO2Eq/₤

 

Paper products

2.87 kgCO2/kg

Others

Construction

0.54 kgCO2Eq/₤

 

Water

0.276 kgCO2Eq/m3

 

Sanitation facilities

0.8 kgCO2Eq/₤

Table 3

Emissions factors for waste collection, recycling, and process

Waste disposal method

Waste category

Emissions factor for conversion to GHG emission (kgCO2Eq/ton)

Incineration

Paper

1800

 

Plastics

 

Rubber

 

Metal

Recycling

Paper

−713

 

Plastics (film)

−1000

 

Plastics (dense)

−1500

 

Cardboard

−713

Landfill

Plastics

40

 

Metal

10

Results

PD regimes with different dialysis modalities were listed in Table 4. The carbon footprints and constituent percentage of PD with different modalities are shown in Table 5. The fixed emissions were higher in patients who received PD therapy in center than at home, mostly attributing to the consumption of electricity. Conversely, PD treatment performed in center yielded less emission than that of at home, which resulted from reduced constituent percentage of waste disposal and transportation. Totally, PD treatment in center produced less carbon footprints than at home, showing an advantage in reducing GHG by medical disposal. Of note, the actual impact of PD on GHG emissions could be underestimated to some extent since waste disposal generated in the manufacturing process was not taken into account, including reuse, recycling, and sale. At the same time, the amount of raw materials was generally supposed to exceed the final quantity of PD products in the manufacturing process.
Table 4

PD regimes with different dialysis modalities

Modality

Treatment site

Dialysate volume (l/day)

No. of subjects

DAPD

Home

6

13

DAPD

Home

8

10

CAPD

Home

6

16

CAPD

Home

8

21

DAPD

Hospital

6

1

DAPD

Hospital

8

4

CAPD

Hospital

6

1

CAPD

Hospital

8

2

Table 5

Carbon footprints and constituent percentage of PD application to individual patient with different modalities and regimes

Category

Source

DAPD emissions (kgCO2Eq)

CAPD emissions (kgCO2Eq)

Home (%)

PD center (%)

Home (%)

PD center (%)

Fixed emissions

Electricity

20.2 (5.0)

32.7 (9.0)

20.2 (4.9)

32.7 (9.0)

 

Paper (office)

0.7 (0.2)

0.7 (0.2)

0.7 (0.2)

0.7 (0.2)

Variable emissionsa

Electricity

6.7 (1.6)

6.7 (1.6)

Packaging

323.4 (79.4)

323.4 (89.0)

323.4 (79.0)

323.4 (88.6)

 

Paper (towel)

10.45 (2.6)

10.45 (2.9)

10.45 (2.6)

10.45 (2.9)

 

Laundry

3.65 (0.9)

3.65 (1.0)

3.65 (0.9)

3.65 (1.0)

 

Waste disposal

33.8 (8.3)

−7.4 (2.0)

33.6 (8.2)

−5.9 (1.6)

Random variable emissionsb

Transportation

8.2 (2.0)

0 (0.0)

10.8 (2.6)

0 (0.0)

Totalc

407.1 (100)

363.5 (100)

409.5 (100)

365 (100)

aEmissions for every two-litter increase in dialysate volume; b data presented as median (percentage), n = 23 for at-home DAPD, n = 37 for at-home CAPD; c total emissions of two-litter dialysate consumption

Discussions

In this study, we reveal an important environmental impact of PD therapy according to different modalities and application sites, which, for the first time provides informative knowledge on the carbon footprints of healthcare delivery for PD therapy. Our findings indicate in-center PD has advantages in reducing GHG emissions over at-home treatment, which might be attributed to the more unified and rational performance of disinfection, dialysate heating, and waste disposal in hospital. Given the increasing concern about the environmental aspects arising from the provision of renal replacement therapy, our data also suggest that the comprehensive environmental analysis for both PD and HD treatment should be fully considered for making national healthcare strategies.

Current carbon footprint research in healthcare has focused largely on the GHG emissions from supply procurement, waste disposal, patient and healthcare staff travel [20, 21, 22]. Travel to and from healthcare facilities, excluding any direct healthcare activities that occur within, accounts for as much as 38% of the GHG emissions associated with healthcare services [22]. In line with these existing data, our findings also demonstrate reduction in transportation largely contributes to the less variable emissions of carbon footprints in in-center PD. Of note, the frequency of such travel might vary considerably between individual patients, although it appears to have a minimal effect on the overall results. This study is also consistent with other RRT investigations of emissions, which highlight that pharmaceuticals and medical equipment emissions dominate the carbon footprint of health services [7, 8, 23, 24]. Of note, energy consumption and landfill waste production account for the majority of the environmental effect of the healthcare industry [25, 26, 27, 28]. Particularly, packaging materials alone account for up to 40% of regulated medical waste [29]. In this study, we show that the packaging consumption in PD treatment contributes to the carbon footprints to an even higher extent of more than 80%, suggesting the potential reductions in GHG emissions arising from cutting down packaging materials in PD treatment, which will require significant participation with producers and manufacturers.

Conventional HD is an especially power- and water-hungry therapy [8]. It is of intriguing to compare the GHG emissions generated by HD and PD therapy. However, the comprehensive data remain lacking in the world for direct comparison of the two RRT treatments. Recent studies in developed countries highlight the impact of different maintenance HD modalities and regimes on the carbon footprints. In the UK, the provision of HD generates an annual per-patient carbon footprint of from 4000 to 7000 kgCO2Eq [7, 23]. Similarly, the annual per-patient carbon footprint of satellite conventional HD is 10,000 kgCO2Eq in Australia [24]. Based on our data in this study, we estimate an annual carbon footprint of ~1400 kgCO2Eq for each at-home PD patient in China with a daily dialysate dose of eight litters. According to these observations, PD treatment may exert potentials of reducing carbon footprints over HD. However, it is also worth noting the different industrialization between China and developed countries. After normalization to the total population [30, 31], we anticipate the per-capita CO2 emissions for the UK and Australia to exceed the China, which could be attributed to the variations existing in electricity generation methods as well as energy requirements for water supply and wastewater disposal. Consequently, the contributions of electricity and water consumption to the carbon footprints may vary in currently available studies. As electricity and water consumption are the major emission contributors in both PD and HD, the local influence factors should be taken into account while comparing different RRT therapeutics.

Furthermore, it is worth noting that significant difference of energy consumption could exist among countries in the world. For example, waste disposal in Australia could have a significantly larger footprint because wastewater undergoes extensive treatment, and treated wastewater is transported across long distances over higher terrain for ocean disposal [24, 32]. This is the first study to investigate the impact of RRT treatment on GHG emissions in China. The significant variation in the carbon intensity of electricity supply throughout China is attributable to the different fuel sources or technologies used for electricity generation [2, 31]. Therefore, we recommend that state-level data be made available, and suggest that state-level emissions planning and target setting is more appropriate in the Chinese context. As similar regional variability may also exist in other countries, region-specific carbon footprint data—especially the data associated with the provision of power and water—need greater appreciation. Moreover, our study also highlights the need for more complete and current sources of Chinese emissions factors, particularly for the procurement sector. The establishment of a comprehensive carbon footprinting framework would assist the Chinese healthcare sector’s transition to a low-emissions industry. Such a framework would enable healthcare institutions to calculate their carbon footprints in a standard manner that minimizes effort and enables progress monitoring.

At the same time, we do recognize that our study has several limitations. Firstly, this is a single-center study performed in a limited number of subjects of Han Chinese ethnicity, which might be insufficient to draw the same conclusion in other populations due to the different lifestyles of energy consumption. Secondly, the small size of study population may distort our results if the subjects are not representative of the statewide or nationwide population of PD patients. Thirdly, there is some uncertainty in the estimation of electricity consumption as no standard methods are currently available. Differing energy-use profiles will also affect the calculated carbon footprint. The methods to normalize GHG information related to different HD machines remain lacking in our center. It remains technically difficult to directly compare the GHG between PD and HD. Lastly, we did not include automated PD (APD) in this study as the enrolled number of APD subjects is small. It is intriguing to evaluate the carbon footprints with different PD models in the future.

Conclusions

In this study, we show that the delivery of PD is associated with considerable GHG emissions, which is largely attributed to packaging consumption. Our data also indicate PD treatment in center produced less carbon footprints than at home, which results from reduced constituent percentage of waste disposal and transportation. This study indicates that PD treatment might exert potentials of reducing carbon footprints in RRT therapies; however, it is also worth noting that the nature of carbon footprinting methodologies in PD is based on several assumptions that might exert an influence on the results. And the PD-associated carbon footprinting is sensitive to regional carbon-intensity variations for electricity and water consumption. Collectively, this study provides timely insight into the GHG emissions of PD treatment, which could be informative for making strategies to reduce the carbon footprinting through the determination of RRT modalities and regimes.

Notes

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.

References

  1. 1.
    Costello A, Abbas M, Allen A, Ball S, Bell S, Bellamy R et al (2009) Managing the health effects of climate change: Lancet and University College London Institute for Global Health Commission. Lancet 373(9676):1693–1733CrossRefPubMedGoogle Scholar
  2. 2.
    Liu X, Zhang Y, Han W, Tang A, Shen J, Cui Z et al (2013) Enhanced nitrogen deposition over China. Nature 494(7438):459–462CrossRefPubMedGoogle Scholar
  3. 3.
    Markandya A, Armstrong BG, Hales S, Chiabai A, Criqui P, Mima S et al (2009) Public health benefits of strategies to reduce greenhouse-gas emissions: low-carbon electricity generation. Lancet 374(9706):2006–2015CrossRefPubMedGoogle Scholar
  4. 4.
    Baillie J (2012) Surveys show support for green ‘activities’. Health Estate 66(3):17–20Google Scholar
  5. 5.
    McGain F (2010) Sustainable hospitals? An Australian perspective. Perspect Public Health 130(1):19–20CrossRefPubMedGoogle Scholar
  6. 6.
    Agar JW (2013) It is time for “green dialysis”. Hemodial Int 17(4):474–478CrossRefPubMedGoogle Scholar
  7. 7.
    Connor A, Lillywhite R, Cooke MW (2011) The carbon footprints of home and in-center maintenance hemodialysis in the United Kingdom. Hemodial Int 15(1):39–51CrossRefPubMedGoogle Scholar
  8. 8.
    Agar JW (2012) Personal viewpoint: hemodialysis–water, power, and waste disposal: rethinking our environmental responsibilities. Hemodial Int 16(1):6–10PubMedGoogle Scholar
  9. 9.
    Jain AK, Blake P, Cordy P, Garg AX (2012) Global trends in rates of peritoneal dialysis. J Am Soc Nephrol 23(3):533–544CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Sinnakirouchenan R, Holley JL (2011) Peritoneal dialysis versus hemodialysis: risks, benefits, and access issues. Adv Chronic Kidney Dis 18(6):428–432CrossRefPubMedGoogle Scholar
  11. 11.
    Li PK, Chow KM (2013) Peritoneal dialysis-first policy made successful: perspectives and actions. Am J Kidney Dis 62(5):993–1005CrossRefPubMedGoogle Scholar
  12. 12.
    Berger A, Edelsberg J, Inglese GW, Bhattacharyya SK, Oster G (2009) Cost comparison of peritoneal dialysis versus hemodialysis in end-stage renal disease. Am J Manag Care 15(8):509–518PubMedGoogle Scholar
  13. 13.
    Baboolal K, McEwan P, Sondhi S, Spiewanowski P, Wechowski J, Wilson K (2008) The cost of renal dialysis in a UK setting—a multicentre study. Nephrol Dial Transplant 23(6):1982–1989CrossRefPubMedGoogle Scholar
  14. 14.
    Lee H, Manns B, Taub K, Ghali WA, Dean S, Johnson D et al (2002) Cost analysis of ongoing care of patients with end-stage renal disease: the impact of dialysis modality and dialysis access. Am J Kidney Dis 40(3):611–622CrossRefPubMedGoogle Scholar
  15. 15.
    BSI (British Standards Institute) (2011) PAS 2050: 2011. Specification for the assessment of the life cycle greenhouse gas emissions of goods and services. British Standards Institute, London. http://shop.bsigroup.com/upload/Shop/Download/PAS/PAS2050.pdf
  16. 16.
    Kyoto protocol reference manual on accounting of emissions and assigned amount. http://unfccc.int/resource/docs/publications/08_unfccc_kp_ref_manual.pdf
  17. 17.
    University of Bath (2009) Inventory of carbon and energy (ICE). http://www.circularecology.com/embodied-energy-and-carbon-footprint-database.html
  18. 18.
    Yanming J (2011) The provincial power industry carbon emissions situation and trend analysis in China. Electr Power Technol Econ 10(23):56–60Google Scholar
  19. 19.
    The Department of Energy and Climate Change (DECC) and the Department for Environment, Food and Rural Affairs (Defra) (2012) Guidelines to Defra/DECC’s GHG conversion factors for company reporting. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/69554/pb13773-ghg-conversion-factors-2012.pdf
  20. 20.
    Morris DS, Wright T, Somner JE, Connor A (2013) The carbon footprint of cataract surgery. Eye (Lond) 27(4):495–501CrossRefGoogle Scholar
  21. 21.
    Venkatesh R, van Landingham SW, Khodifad AM, Haripriya A, Thiel CL, Ramulu P et al (2016) Carbon footprint and cost-effectiveness of cataract surgery. Curr Opin Ophthalmol 27(1):82–88CrossRefPubMedGoogle Scholar
  22. 22.
    Pollard AS, Taylor TJ, Fleming LE, Stahl-Timmins W, Depledge MH, Osborne NJ (2013) Mainstreaming carbon management in healthcare systems: a bottom-up modeling approach. Environ Sci Technol 47(2):678–686CrossRefPubMedGoogle Scholar
  23. 23.
    Connor A, Lillywhite R, Cooke MW (2010) The carbon footprint of a renal service in the United Kingdom. QJM 103(12):965–975CrossRefPubMedGoogle Scholar
  24. 24.
    Lim AE, Perkins A, Agar JW (2013) The carbon footprint of an Australian satellite haemodialysis unit. Aust Health Rev 37(3):369–374. doi:10.1071/AH13022 CrossRefPubMedGoogle Scholar
  25. 25.
    DiConsiglio J (2008) Reprocessing SUDs reduces waste, costs. Mater Manag Health Care 17(9):40–42PubMedGoogle Scholar
  26. 26.
    Hawkes N (2012) Cutting emissions by drug industry is crucial to reducing NHS’s carbon footprint. BMJ 345:e8243CrossRefPubMedGoogle Scholar
  27. 27.
    Simpson M (2008) Reducing NHS carbon footprint: time for a culture change. BMJ 336(7649):848CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Woods DL, McAndrew T, Nevadunsky N, Hou JY, Goldberg G, Yi-Shin Kuo D et al (2015) Carbon footprint of robotically-assisted laparoscopy, laparoscopy and laparotomy: a comparison. Int J Med Robot 11(4):406–412CrossRefPubMedGoogle Scholar
  29. 29.
    Lee BK, Ellenbecker MJ, Moure-Eraso R (2002) Analyses of the recycling potential of medical plastic wastes. Waste Manag 22(5):461–470CrossRefPubMedGoogle Scholar
  30. 30.
    Lenzen M (2008) Life cycle energy and greenhouse gas emissions of nuclear energy: a review. Energy Convers Manag 49(8):2178–2199CrossRefGoogle Scholar
  31. 31.
    The Revision of China’s Energy and Coal Consumption Data: A preliminary analysis (2015). http://www.theenergycollective.com/hao-tan/2292551/revision-china-s-energy-and-coal-consumption-data-preliminary-analysis
  32. 32.
    Kenway SJ PA, CookS, Seo S, Inman M, GregoryA (2008) Energy use in the provision and consumption of urban water in Australia and New Zealand. CSIRO: water for a Healthy Country National Research Flagship. https://publications.csiro.au/rpr/download?pid=csiro:EP116078&dsid=DS1

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Mindong Chen
    • 1
  • Rong Zhou
    • 1
  • Chongbo Du
    • 2
  • Fulei Meng
    • 2
  • Yanli Wang
    • 2
  • Liping Wu
    • 2
  • Fang Wang
    • 2
  • Yahong Xu
    • 1
  • Xiufen Yang
    • 2
  1. 1.Department of Nephrology, Yangpu HospitalTongji UniversityShanghaiChina
  2. 2.Department of Intensive Care UnitThe First Hospital of Hebei Medical UniversityShijiazhuangChina

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