Ecological footprint assessment and its reduction for industrial food products

ABSTRACT

The food processing industries significantly contribute to the consumption of fossil energy, materials, and other resources with commensurate GHG emissions. In this study, the Ecological Footprint (EF) assessment of four different food products (Pickles, Jams, Honey and Chutney) manufactured in a food processing industry (Merry Foods) in India has been presented. Simultaneously, the reduction potential in terms of the EF of the food products has also been estimated by suggesting the installation of some sustainable technologies.

The ecological footprint of packaged pickle, jam, honey and chutney products have been estimated as 1.67, 0.54, 15.94 and 2.18 gha/ton, respectively. The overall bio-productive land that had the maximum involvement for the food products’ processing at the industry was Cropland (85.7%) followed by CO₂ absorption land (11.2%). The life cycle EF of suggested sustainable systems such as grid connected rooftop solar PV system, biogas plant, and rooftop rainwater harvesting system has been estimated as 56.18 gha, 1.33 gha, and 2.92 gha, respectively. Hence, the EF reduction potential of the food products by using all the suggested sustainable technologies was estimated as 99.6 gha. This study is a step towards the sustainability of industrial food products.

Abbreviations: EF: Ecological footprint; GRSPV: Grid-connected rooftop photovoltaic; C&D: Construction and demolition; RRH: Rooftop rainwater harvesting; LCEF: Life-cycle ecological footprint; GDP: Gross domestic product; gha: Global hectare; e_i: Equivalence factor; LCA: Life cycle analysis; EF_{food product}: Ecological footprint of food products; EF_e: Ecological footprint of energy consumption; EF_w: Ecological footprint of water consumption; EF_t: Ecological footprint of transportation; EF_we: Ecological footprint of C&D waste disposal; EF_m: Ecological footprint of manpower; EF_direct: Ecological footprint of direct land consumption; GHG: Greenhouse gas; LPG: Liquefied petroleum gas; EF_me: Ecological footprint of material consumption; HDV: Heavy duty vehicle.

KEYWORDS:

• Ecological footprint
• food product sustainability
• environmental assessment
• renewable energy system

1. Introduction

In India, the industrial sector accounts for approximately 36.5% (382.7 TWh) of the total electricity consumption (MPCEA 2018), 1.77 Mt of LPG consumption, while the total global biomass consumed in the industry was estimated to be about 7.7 exajoules (EJ) in the year 2015(IEA, 2017).Food production significantly contributes to the consumption of energy, materials, and natural resources as well as leaves a remarkable environmental imprint on the planet. The food processing industry is estimated to be around USD 180 billion and contributes about 1.3–1.5% to India’s GDP. Processing and preserving of fruits and vegetables in India engages 58,331 people in 1101 factories (MOFPI, 2017).

A well-developed food processing sector with a higher level of processing helps in the reduction of wastage, improves value addition, promotes crop diversification, ensures a better return to the farmers, promotes employment as well as increases export earnings. On a global scale, 1.3 × 10⁹ ton of food, is wasted annually, which amounts to approximately a third of the food produced for humans (Girotto, Alibardi, and Cossu 2015). Fruit juice facilities often produce peels, seeds, and other organic matter as waste (Qu et al. 2009; John, Muthukumar, and Arunagiri 2017). Nowadays, food-waste handling is a major challenge; in general food waste is disposed of as landfills, which is not a sustainable solution for waste handling (USEPA, 2016; Babbitt CW, 2017). RedCorn, Fatemi, and Engelberth (2018) reported that industrial food waste has the potential to reduce approximately 1.9 × 10⁸ ton of CO_2eq emissions if it is converted to a higher value product (RedCorn, Fatemi, and Engelberth 2018). Opatokun et al. (2017) evaluated that the industrial anaerobic digestion, pyrolysis and integration of anaerobic digestion and pyrolysis processes may produce 0.369 kWh, 0.020 kWh and 0.365 kWh of heat per kg of food waste, respectively. Jin et al. (2015) reported that food waste may be responsible for 96.97 kgCO_2-eq emissions per ton if it is disposed of as a landfill. However, if it is utilised for biogas generation, then it may generate 2630 MJ of energy per ton of waste.

Some researchers have previously reported the environmental impact of various food products. Wiedemann, McGahan, and Murphy (2017) reported that the total greenhouse gas emissions for 1 kg of the boneless chicken portion was estimated to be 4.78 kg CO_2-eq and 3.9 kg CO_2-eq, for two regions Queensland and South Australia, respectively. For organic, conventional and integrated apple production systems, the ecological footprint was 0.08 gha/ton, 0.34 gha/ton and 0.25 gha/ton, respectively (Limnios et al. 2009). Ghasempour and Ahmadi (2016) reported that 1 kg of egg production was responsible for 4.09 kg CO_2-eq for the Alborz province in Iran. The ecological footprint of biscuit production in the city of York is about 3.3 gha/ton (Barrett et al. 2002). Świąder et al. (2018) have reported that the Ecological Footprint of food exceeded the biocapacity of the city of Wrocław, Poland by 10 times. Therefore, the environmental impact of food production should be reduced so that human activities pertaining to food consumption don’t exceed the carrying capacity of the planet.

Various techniques may be utilised to reduce the environmental impacts of the products manufactured in the industry. Various researchers have reported that the utilisation of renewable systems. Roof-top solar PV systems are applied in industrial complexes that help to address energy sustainability issues. In India, most of the industries meet their electricity demand through the grid. If industries use solar PV systems, it has potential to significantly reduce the environmental impact of the product because PV on an average reduces the emission up-to 0.7 kg CO₂ per kWh electricity generated (Herzog 1999).

Omar and Mahmoud (2018) have studied the technical performance and economic feasibility of grid-connected rooftop solar PV systems in Palestine and have reported that the system has a payback of 4.9 years and an annual yield of 1756 kWh/kWp. Ali, Shafiullah, and Urmee (2018) have explored the possibility of installing a roof-mounted solar PV system in Maldives and have found out that there is potential for generating 4.8 GWh to 8.0 GWh of electricity from rooftop PV systems in a year. Brunklaus et al. (2018) have performed an LCA study of converting food waste in Sweden into biogas by volarization techniques. Giacchetta, Leporini, and Marchetti (2014) performed the technical and economic analysis of different cogeneration systems based on biogas and they have reported that the thermal energy sale price was about 0.025 €/kWh_t. Florkowskia, Usb, and Klepacka (2018) have studied the conversion of food waste to biogas in Poland. Müller, Brandmayr, and Zörner (2014) have developed and evaluated a methodology for the potential of solar-thermal energy use in the food industry and they reported that 6.4% of the thermal energy demands of dairies can be met through solar thermal systems. Ghimire et al. (2017) have done the LCA of a commercial rainwater harvesting system and have also compared it to the Municipal water supply system. They have found out that the rainwater harvesting system is superior to the municipal water supply system in many respects. Lee et al. (2016) have proposed rainwater harvesting system to mitigate water crisis in Malaysia.

The Ecological Footprint (EF) concept was developed in the mid-nineties by Mathis Wackernagel and William Rees (Wackernagel and Rees 1996). The indicator incorporates all inputs (resources) and converts them into a single parameter called ‘global hectare (gha)’. One global hectare (gha) is equivalent to 1 ha of bio-productive land with world average productivity (Bastianoni et al. 2006). The expression for evaluating EF (in gha) is as follows (GFN, 2010):

$EcologicalFootprint\left(EF\right)=\sum \left(C_i/Y_i\right).e_i$

where, C_i is annual consumption of item i (kg/yr), Y_i is annual productivity of item i (kg/ha), e_i represents the equivalence factor of different land types (such as cropland, pastureland, forestland, marine land, etc.) and its values are depicted in Table 1. Carbon sequestration due to human activities (such as fossil fuel burning, emissions during material production, transportation, etc.) can be expressed as follows (GFN 2010):

$EFofcarbonsequestration=P_c\left\{\left(1-A_{oc}\right)/A_f\right\}.e_i$

Table 1. Equivalence factor (GFN 2016)

Land type	Equivalence factor ei (gha/ha)
CO2 absorption land (eCO2 land)	1.28
Forest land (eforest land)	1.28
Crop land (ecropland)	2.52
Built-up land (ebuilt-up land)	2.52
Pasture land (epasture land)	0.43
Sea productive/marine land (emarine land)	0.35

where, P_C is annual CO₂ emissions, A_oc is the fraction of annual oceanic anthropogenic CO₂ sequestration and A_f is the annual rate of carbon uptake per hectare of forestland at world average yield (GFN 2010).

According to Global Footprint Network, the total Ecological Footprint and per capita Ecological Footprint of India is 1.36 billion gha and 1.1 gha/person, respectively; while the bio-capacity deficit of the country is 0.7 gha/person (GFN 2017). Indian resource demands have already surpassed the available bio-capacity of the country.

In this paper, the EF of four different manufactured food products of a food processing industry (Merry Foods) located at Allahabad (UP), India has been assessed. Simultaneously, various measures such as Grid-connected rooftop solar PV system, biogas plant and rainwater harvesting system have been suggested to reduce the EF of all the products. The Life-Cycle Ecological Footprint (LCEF) of suggested sustainable systems has also been calculated to find out the net EF reduction. This study will be helpful for estimating the environmental impact of Indian food processing industries and providing an approach towards sustainable food processing in India by reducing its EF.

1.1. Aim and scope

The environmental impact of various food products should be assessed and various steps should be adopted for their possible impact reductions through the installation of sustainable systems. The ecological footprint of the food product should be mentioned on the packaging of the product; because it incorporates all the resource consumption that has a direct or indirect involvement for the manufacturing of the food product. It will facilitate the consumer to select a product amongst a large number of similar products. Also, it may act as a reference for policymakers to link the environmental impact of the industry with their taxation and loans.

This type of environmental labelling on the food packaging will act as an encouragement to the manufacturers to reduce their environmental impacts, which can be helpful to reduce the global industrial ecological footprint.

2. Methodology

A methodology has been proposed for assessing and reducing the ecological footprint of a food product being manufactured in a food processing industry. The detailed steps of the proposed methodology are as follows:

2.1. Ecological footprint of a food product (EF_{food product})

The industrial ecology considers the production of finished goods, distribution, and consumption of resources and services including the utilisation of direct energy and waste disposal, etc. In order to evaluate EF_{food product}, all the resources related to a production system are converted into their equivalent bio productive land that is needed to produce or absorb their impacts in the form of land categories (such as CO₂ land, forestland, cropland, built-up land, etc.). The system boundary of the food processing industry is shown in Figure 1. All the resources consumed during the manufacturing of the final products, steps involved in the processing of the food products, waste generation and corresponding transportation are depicted in Figure 1. All the factors influencing the EF_{food product} and involvement of different types of bio-productive land are shown in Figure 2. The estimation of EF_{food product} is given by the Equation (1)(1) $\mathrm{E}{\mathrm{F}}_{\mathrm{f}\mathrm{o}\mathrm{o}\mathrm{d}\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}}=\mathrm{E}{\mathrm{F}}_{\mathrm{e}}+\mathrm{E}{\mathrm{F}}_{\mathrm{m}\mathrm{e}}+\mathrm{E}{\mathrm{F}}_{\mathrm{w}\mathrm{e}}+\mathrm{E}{\mathrm{F}}_{\mathrm{t}}\text{break}+\mathrm{E}{\mathrm{F}}_{\mathrm{w}}+\mathrm{E}{\mathrm{F}}_{\mathrm{m}}+\mathrm{E}{\mathrm{F}}_{\mathrm{d}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{d}}$(1) :

(1) $\mathrm{E}{\mathrm{F}}_{\mathrm{f}\mathrm{o}\mathrm{o}\mathrm{d}\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}}=\mathrm{E}{\mathrm{F}}_{\mathrm{e}}+\mathrm{E}{\mathrm{F}}_{\mathrm{m}\mathrm{e}}+\mathrm{E}{\mathrm{F}}_{\mathrm{w}\mathrm{e}}+\mathrm{E}{\mathrm{F}}_{\mathrm{t}}\text{break}+\mathrm{E}{\mathrm{F}}_{\mathrm{w}}+\mathrm{E}{\mathrm{F}}_{\mathrm{m}}+\mathrm{E}{\mathrm{F}}_{\mathrm{d}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{d}}$(1)

Figure 1. The production process of the food industry

Figure 2. Ecological footprint of the final product

Here, EF_e, EF_me, EF_we, EF_t, EF_w, EF_m and EF_built-up represent the Ecological Footprint of product’s energy consumption, material consumption, waste generation, transportation, water consumption, manpower, and direct land consumption of the final product, respectively, which cumulatively assesses the EF of a food product. All the above parameters have been further discussed below:

2.1.1. Ecological footprint of energy consumption (EF_e)

The EF_e has been determined by the direct energy-related emission during the manufacturing of a food product. The EF_e of a food product is calculated by using Equation (2)(2) $E{F}_e=\sum C_{\mathrm{e}\mathrm{i}}.{\lambda }_{\mathrm{i}}.\left(\frac{1-Aoc}{Af}\right)e_{C{O}_{2}land}$(2) :

(2) $E{F}_e=\sum C_{\mathrm{e}\mathrm{i}}.{\lambda }_{\mathrm{i}}.\left(\frac{1-Aoc}{Af}\right)e_{C{O}_{2}land}$(2)

where C_ei is direct energy consumption during the manufacturing process of the food product; λ_i is the corresponding emission factor of i^th fuel (i.e. diesel, LPG, electricity); A_oc (i.e. 0.3) represents percentage of CO₂ absorption in oceans (SIO 2017), A_f (i.e. 2.68 tCO₂/ha) is CO₂ absorption factor of forests (Mancini et al. 2016). The term$e_{C{O}_2}$_land is the equivalence factor of CO₂ absorption land listed in Table 1.

2.1.2. Ecological footprint of materials (EF_me)

(3) $E{F}_{me}=\sum \frac{\left(1+a\right)C_{mi}}{{Y_C}_{mi}}.e_i+\sum \left(P_{mj}.{\lambda }_j\right).\left(\frac{1-Aoc}{Af}\right)e_{C{O}_{2}land}$(3)

where, C_mi is the consumption of the i^th natural raw material during the processing of the food product;${Y_C}_{mi}$ is the productivity of i^th natural raw material; ‘a’ is waste factor of raw material; P_mj is the j^th manufactured product consumed during food processing; λ_j is corresponding embodied emission factor of j^th material;

2.1.3. Ecological footprint of transportation (EF_t)

The EF_t depends on three parameters: first, materials transportation from the cultivation site to the industrial location; second, manpower transportation from their houses to the industrial location; and third, waste disposal from the industry to landfill area/recycling plant. The data for the estimation of the EF_t based on survey and response to questionnaires are as follows:

1. Materials and wastes are transported through Heavy Duty Vehicles (HDV).

2. Labourers use diesel-fuelled buses to reach the site of the industry from their houses.

3. The average distance travelled by labourers is 5–10 km while materials and wastes are transported to a distance of 10–15 km.

Estimation of the EF_t of material, manpower and wastes is done by Equation (4)(4) $\begin{array}{rl}& E{F}_t=\left\{\left(\sum \frac{C_{mi}.D_{mi}}{T_c}+\sum \frac{C_{wj}.D_{wj}}{T_c}\right).{\mathrm{E}}_{\mathrm{H}\mathrm{D}\mathrm{V}}+\sum \frac{M_k.D_{mk}}{T_b}{E}_{bus}\right\}\\ & .{\lambda }_{fuel}.\left(\frac{1-{\mathrm{A}}_{oc}}{Af}\right)e_{C{O}_{2}land}\end{array}$(4) :

(4) $\begin{array}{rl}& E{F}_t=\left\{\left(\sum \frac{C_{mi}.D_{mi}}{T_c}+\sum \frac{C_{wj}.D_{wj}}{T_c}\right).{\mathrm{E}}_{\mathrm{H}\mathrm{D}\mathrm{V}}+\sum \frac{M_k.D_{mk}}{T_b}{E}_{bus}\right\}\\ & .{\lambda }_{fuel}.\left(\frac{1-{\mathrm{A}}_{oc}}{Af}\right)e_{C{O}_{2}land}\end{array}$(4)

where, Cm_i and Dm_i are consumption and the average distance of transportation of i^th material, respectively. Cw_j and Dw_j are waste generation and the average distance travelled by the j^th material, respectively. M_k and D_mk are the numbers of labourers and the average distance travelled by them, respectively. T_c and T_b represent the capacity of truck (3.5 ton) and bus (50 passenger), respectively; and λ_diesel emission factor of diesel fuel (3.17 CO₂ kg/kg of diesel (EEA 2013). The average fuel efficiency of HDT (E_HDV) and Bus (E_bus) are 0.222 and 0.238 kg of fuel/km, respectively (Baidya and Borken-Kleefeld 2009).

2.1.4. Ecological footprint of labour/manpower (EF_m)

The EF_m is associated with food consumption of labour/manpower because the impact of their mobility/transportation is already included in Section 2.1.3. During work-hours (10 h) nearly 1500 kcal is consumed at the rate of 155.3 kcal/hr (Rocamora, Solís-Guzmán, and Marrero 2017). This is nearly 65% of the daily metabolic calories burned (2400 kcal/day (NIN 2011)) by a labour. The daily diet of labourers is variable and it has not been calculated precisely. Therefore, the National Sample Survey Office (NSSO 2014) report is used to calculate the food consumption of manpower/labourers in India. The annual EF per person for food consumption in India is depicted in Table 2. The per capita annual EF of the total number of labour involved during the manufacturing process of the food products have been obtained by industry survey. The EF_m is determined by Equation (5)(5) $E{F}_m=N.\frac{d_w}{365}.f_d\left\{\sum \left(\frac{C_{f_i}}{Y_{f_i}}\right).e_i+\text{break}\sum \left(C_{fue{l}_j}.{\lambda }_{fue{l}_j}\right).\frac{\left(1-A_{oc}\right)}{A_f}.e_{C{O}_{2}land}\right\}$(5) :

(5) $E{F}_m=N.\frac{d_w}{365}.f_d\left\{\sum \left(\frac{C_{f_i}}{Y_{f_i}}\right).e_i+\text{break}\sum \left(C_{fue{l}_j}.{\lambda }_{fue{l}_j}\right).\frac{\left(1-A_{oc}\right)}{A_f}.e_{C{O}_{2}land}\right\}$(5)

Table 2. Main raw materials for food preparation per capita in India

	Monthly consumption (NSSO, 2014)	Total annual consumption	CO2 emission factor	Yield Production (ton/ha)	Annual EF (gha)
Cereals	9.28 kg	111.36kg		2.39 (OGD, 2017)	0.117
Pulses	0.90 kg	10.8 kg		0.69 (OGD, 2017)	0.039
Vegetable	8.4 kg	100.8		kg 1.61 (OGD, 2017)	0.157
Beef	0.06 kg	0.72 kg		32 (chambers, 2004)	0.011
Mutton	0.08 kg	0.96 kg		72 (chambers, 2004)	0.006
Milk	5.4 litre	64.8 litre		458 (chambers, 2004)	0.062
Fish	0.252 kg	3.024 kg		0.035 (chambers, 2004)	0.030
Fruits	0.654 kg	7.848 kg		(IHD, 2014)	1.58 x10^−6
Edible oil	0.85 kg	10.2 kg		0.38	2.32
Wood	4.3 kg	51.6 kg	1.5- 1.6 (kgCO2/kg)	73 m^3/ha (FSI, 2015)	0.028
LPG	1.9 kg	22.8 kg	3.31 (kgCO2/kg)		0.025
Kerosene	0.40 litre	4.8 litre	2.58 (kgCO2/litre)		0.004
Total annual EF per capita					0.549

where, N is the number of labours involved in food processing, d_w is total number of days per year during which the industry is processing the food product; f_d is daily food intake fraction of an Indian adult (65%); C_fi is food consumption category j (kg/person); and Y_fi is food production yield of i^th material (kg/ha). C_fuelj is fuel consumption category j for meal preparation (kg/person), λ_fuelj is an emission factor of category j fuel.

2.1.5. The ecological footprint of material waste (EF_we)

The material waste generally results in landfill disposal and transportation of the waste materials, while its transportation is included in the Section 2.1.3. However, recycling/reuse of waste materials may reduce ecological impact. The EF_we is determined by Equation (6)(6) $\mathrm{E}{\mathrm{F}}_{\mathrm{w}\mathrm{e}}=\sum \left(1-R-R^{\prime }\right)\frac{C_{w_i}}{Y_{w_i}}.e_{landfill}$(6) :

(6) $\mathrm{E}{\mathrm{F}}_{\mathrm{w}\mathrm{e}}=\sum \left(1-R-R^{\prime }\right)\frac{C_{w_i}}{Y_{w_i}}.e_{landfill}$(6)

where, C_wi represents the amount of waste generated during manufacturing process of the food product (m³), R represents the fraction of waste that is directly reusable and R’ represents the fraction of waste that can be recycled, Y_wi represents the assimilation rate of waste disposal and e_landfill represents the equivalence factor of the type of bio-productive land for the waste to be disposed (0.43 gha/ha pasture land (GFN 2016)).

2.1.6. The ecological footprint of water consumption (EF_w)

The EF_w depends on water consumption during the manufacturing process of a food product. In India, groundwater is generally used in industries; therefore, CO₂ absorption land is needed to compensate for the energy for uplifting water from underground to overhead tanks. The EF_w of water consumption is determined by Equation (7)(7) $\mathrm{E}{\mathrm{F}}_{\mathrm{w}}=C_w.\left\{E_w.{\lambda }_{fuel}.\frac{\left(1-A_{oc}\right)}{A_f}\right\}{e}_{C{O}_{2}land}$(7) :

(7) $\mathrm{E}{\mathrm{F}}_{\mathrm{w}}=C_w.\left\{E_w.{\lambda }_{fuel}.\frac{\left(1-A_{oc}\right)}{A_f}\right\}{e}_{C{O}_{2}land}$(7)

where, C_w is total water consumption during the manufacturing process (m³); E_w is energy consumption to extract groundwater (kWh/m³); and λ_fuel is emission factor of fuel used for groundwater extraction.

2.1.7. The ecological footprint of direct/physical land (EF_{direct land})

This section focuses on the direct/physical land occupied by the food processing industry that is involved for the manufacturing of food products. The EF_{direct land} of a food product is determined by Equation (8)(8) $\mathrm{E}{\mathrm{F}}_{\mathrm{d}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{d}}=\mathrm{A}.{\mathrm{e}}_{directland}\left(\mathrm{g}\mathrm{h}\mathrm{a}\right)$(8) :

(8) $\mathrm{E}{\mathrm{F}}_{\mathrm{d}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{d}}=\mathrm{A}.{\mathrm{e}}_{directland}\left(\mathrm{g}\mathrm{h}\mathrm{a}\right)$(8)

where, A is the area of occupied land for manufacturing of food products within an industry (ha) and e_{direct land} is the equivalence factor of direct/physical land.

2.2 EF reduction measures

Energy and water both are essential for a food industry. Renewable energy systems are a clean power source; however, they consume some energy (associated with materials, transportation, etc.) and therefore are responsible for a small amount of GHG emissions. Therefore, EF analysis of such type of systems should also be included while assessing the overall EF of the food products. The methodology of detailed EF assessment of selected sustainable systems has been given below:

2.2.1. Grid connected roof-top solar PV (GRSPV) system

Various research studies on LCA of different types of solar photovoltaic (SPV) module systems suggest its potential to reduce environmental impact (Prakash and Bansal 1995; Cucchiella, D’Adamo, and Koh 2015). RETScreen Software has been used to simulate the capacity of the GRSPV system for the available rooftop of Merry Foods. In this study, multi-Si based PV module has been suggested because other modules have some constraints (such as material, high degradation rate and hazardous waste disposal (i.e. Cd) of the thin film (CIS, a-Si and CdTe) based solar PV systems, as well as, multi-Si PV systems have the largest market share (51%) due to their competitive cost (IRENA 2016). Details of the suggested GRSPV systems have been discussed in Section 4.2.1.

2.2.2. Biogas plant

Biogas, being carbon neutral, is a more sustainable energy source that can be used in place of fossil fuel-based energy source (Cecchi and Cavinato 2015). One kg of biogas is capable of replacing 0.25 kg of LPG (Agrahari and Tiwari 2013). Ogur and Mbatia (2013) have provided technical details of the biogas plant (digester capacity of 4.8 m³) which provides sufficient biogas to completely meet the heating demands of Merry Foods. Details of the suggested biogas plant have been discussed in Section 4.2.2.

2.3. Roof-top rainwater harvesting (RRH) system

In India, food industries mainly utilise groundwater for processing activities, this can cause the groundwater level to deplete. According to Roof top Rain Water Harvesting 2016 report, rooftop rainwater harvesting system may provide approximately 1.4 m³ water per m² of catchment area in India (Kalimuthu 2016). The water demand of the Indian industrial sector (approx. 35% groundwater consumed by industries) is gradually increasing and it will reach two digits during the next three decades (FICCI 2011). Rainwater harvesting can fulfil the industrial water needs and simultaneously increase the groundwater table. For the suggested RRH system at Merry Foods, technical assistance has been taken from RRWH (2016) report. Details of the suggested RRH has been discussed in Section 4.2.3.

3. Case study

Merry Foods, a food processing industry located at Allahabad, India has been selected for our study. It manufactures four types of food products (Pickles, Jams, Honey and Chutney) in blister packs. The detailed information about Merry Foods has been mentioned in Table 3.

Table 3. Details about Merry Foods industry

Industrial description	Data
Working time:	09:00 AM to 7:00PM
No. of employees:	80 employee/day
Plant area:	5000 m^2
Roof Area:	1350 m^2
Plant production capacity: Pickles	1020 ton/year
Jam	459 ton/year
Honey	54 ton/year
Chutney	30 ton/year
Water consumption:	1000 litre/day
LPG consumption:	23 kg/day
Raw materials used and Additives used:
For Pickle:	Raw vegetables, citric acid, salt and mustard oil
For Jam:	Apple, Strawberry and Preservatives
For Honey:	Natural Honey
For Chutney:	Fruits, vegetables and preservatives
Average kWh electricity usage:	850 kWh/day

4. Results

4.1. The ecological footprint of the food processing industry

The annual ecological footprint of the four products manufactured at Merry Foods has been estimated to be 2884.44gha. The overall bio-productive land (cropland – 85.7%, CO₂ absorption land – 11.2%, forestland – 2.7%, pastureland – 0.23%, Sea productive land – 0.08%, direct land – 0.044%) involvement is depicted in Figure 3(a). The input parameters have been considered individually to estimate the total Ecological Footprint of the food products being manufactured.

Figure 3. (a) Bio-productive land involved in overall food processing at Merry Foods, (b) EF fraction of overall food processing at Merry Foods, (c) Bio productive land for pickle production, (d) Bio productive land for jam production, (e) Bio productive land for chutney production, (f) Bio productive land for honey production

The impact of the different input parameters (such as energy consumption, material and natural resource consumption, manpower, transportation and waste generation) which influence the EF of the food products has been individually calculated and discussed below:

4.1.1 Ecological footprint of energy consumption (EF_e)

The grid electricity and LPG consumption in the industry have been obtained by industry survey and data collection and mentioned in Table 3. The EF_e for pickle, jam, honey and chutney production are 78.59 gha, 43.96 gha, 4.16 gha and 2.31 gha, respectively. The combined EF_e is 129 gha, which is about 4.5% of the total Ecological Footprint of the food products being manufactured at Merry Foods.

4.1.2. Ecological footprint of material consumption (EF_me)

The annual raw material consumed in the industry has been obtained by industry survey and data collection and it is mentioned in Table 4. The EF_me for pickle, jam, honey and chutney production are 1590.2 gha, 185.04 gha, 850.16 gha and 57.08 gha, respectively. The combined EF_me is 2682.5 gha, which is about 93% of the total Ecological Footprint of all four food products. Therefore, the material consumption is the major contributor to the EF of the food products being processed at Merry Foods.

Table 4. Details of material and resource consumption in the industry

Materials	Unit	Consumption	Productivity/ Yield (ton/ha)	Emission factor (λi) ton CO2/ton	EF/item (gha/unit)	EF (gha)
Honey	ton	59.4	0.09 (De-Miguel, Pukkala, and Yeşil 2013)		14.22	844.8
Vegetables	ton	404.25	1.613(OGD 2017)	-	1.56	631.56
Oil	ton	102	0.378	-	6.66	680
Fruits	ton	749.25	12.33(IHD 2014)	-	0.103	77.78
Sugar	ton	137.7	3.23	0.183	0.840	115.7
Spices	ton	130.95	1.85	-	1.363	178.49
Electricity	kWh	306,000	-	0.82 tCO2/MWh (MPCEA 2018)	0.000394	120.43
Water	m^3	340	-	-	0.000853	0.290
Packaging(PP virgin)	ton	103.4	-	4.49	1.49	154.06
LPG		7820	-	3.312 (BEC, 2017)	0.0011	8.59
Transportation
Materials (HDT)	km-kg	-	-	3.17 kgCO2/kg of fuel, 0.24 kg fuel/km (EEA 2013)	4.2 x 10^−6	13.8
Waste (HDT)	km-kg	-	-		4.2 x 10^−6	2.07
Manpower (Bus)	km-passenger	-	-	3.17 kgCO2/kg of fuel, 0.238 kg fuel/km (EEA 2013)	2.04 x 10^−6	11.08

4.1.3. Ecological footprint of transportation (EF_t)

The EF_t for pickle, jam, honey and chutney production are 17.6 gha, 7.93 gha, 0.93 gha and 0.52 gha, respectively. This is about 0.9% of the total Ecological Footprint of the food products being manufactured at Merry Foods.

4.1.4. Ecological footprint of labour/manpower (EF_m)

The EF_m for pickle, jam, honey and chutney production are 21.94 gha, 10.97 gha, 5.49 gha and 5.49 gha, respectively. This is about 1.5% of the total Ecological Footprint of the food products being manufactured at Merry Foods.

4.1.5. Ecological footprint of waste generation (EF_we)

The annual waste generation of the industry has been obtained by survey and data collection and is found out to be 237.5 ton. The EF_we for pickle, jam, honey and chutney production are 0.303 gha, 0.14 gha, 0.02 gha and 0.01 gha, respectively. This is about 0.016% of the total Ecological Footprint of the food products being manufactured at Merry Foods.

4.1.6. Ecological footprint of water (EF_w)

The EF_w for pickle, jam, honey and chutney production are 0.19 gha, 0.09 gha, 0.01 gha and 0.006 gha, respectively. The combined EF_w calculated is about 0.3 gha, which is 0.01% of the total EF of the food products being manufactured at Merry Foods.

4.1.7. Ecological footprint of direct land (EF_{direct land})

The EF_{direct land} for pickle, jam, honey and chutney production are 0.82 gha, 0.37 gha, 0.04 gha and 0.02 gha, respectively. The combined EF_{direct land} calculated is about 1.26 gha, which is 0.04% of the total EF of the food products being manufactured at Merry Foods.

4.1.8. Distribution of the components of EFfood product

The contribution of pickle, jam, honey and chutney production is 59.27%, 8.61%, 29.84% and 2.26%, respectively, of the total EF of the food products (Figure 3(b)). The contribution of all bio-productive land types for the four food products manufactured at Merry Foods is depicted in Figure 3(c–f). The significant land types involved in the EF_{food product} are cropland and CO₂ absorption land (i.e. 85.7% and 11.2% of the total bio-productive land, respectively). The ecological footprint of pickle, jam, honey and chutney finished products are 1.67, 0.54, 15.94 and 2.18 gha per ton, respectively. The contribution of all bio-productive land types for different food products (Pickles, Jams, Honey and Chutney) are depicted in Tables 5–8.

Table 5. Ecological footprint for pickle production

	Land type(gha)
	CO2 absorption land	Cropland	Forest land	Pasture land	Sea-productive land	Direct land	EF(gha)	Percentage (%)
EFe	78.59						78.59	4.60
EFme	101.32	1436.31	52.58				1590.21	93
EFt	17.62						17.62	1.03
EFm	2.24	15.28	0.0804	3.14	1.21		21.94	1.28
EFwe				0.30			0.303	0.017
EFw	0.19						0.19	0.01
EFdirect land						0.82	0.82	0.048
Total EF							1709.69	100

Table 6. Ecological footprint for jam production

	Land type(gha)
	CO2 absorption land	Cropland	Forest land	Pasture land	Sea-productive land	Direct land	EF (gha)	Percentage (%)
EFe	43.96						43.96	17.69
EFme	53.96	107.43	23.66				185.04	74.46
EFt	7.93						7.93	3.19
EFm	1.12	7.64	0.040	1.57	0.60		10.97	4.41
EFwe				0.14			0.14	0.054
EFw	0.09						0.09	0.034
EFdirect land						0.37	0.37	0.148
Total EF							248.5	100

Table 7. Ecological footprint for honey production

	Land type(gha)
	CO2 absorption land	Cropland	Forest land	Pasture land	Sea-productive land	Direct land	EF (gha)	Percentage (%)
EFe	4.16						4.16	0.48
EFme	5.36	844.8					850.16	98.76
EFt	0.93						0.93	0.11
EFm	0.56	3.82	0.020	0.78	0.30		5.49	0.64
EFwe				0.02			0.02	0.002
EFw	0.01						0.01	0.001
EFdirect land						0.04	0.04	0.005
Total EF							860.81	100

Table 8. Ecological footprint for chutney production

	Land type(gha)
	CO2 absorption land	Cropland	Forest land	Pasture land	Sea-productive land	Direct land	EF (gha)	Percentage (%)
EFe	2.31						2.31	3.53
EFme	1.79	53.74	1.55				57.08	87.23
EFt	0.52						0.52	0.79
EFm	0.56	3.82	0.02	0.78	0.3024		5.49	8.38
EFwe				0.01			0.01	0.01
EFw	0.006						0.006	0.008
EFdirect land						0.02	0.02	0.036
Total EF							65.43	100

In this study, the analysis of the environmental impact of the packaging has also been performed, and it is estimated to be about 1.4 gha per ton of final food product (i.e. 7.6% of the total EF of the food products).

4.2 Ecological footprint reduction measures

The proposed systems for the Merry Foods Industry are depicted in the Figure 4.If all the suggested measures are installed in Merry Foods, it may reduce the total EF of the food products by about 99.6 gha (i.e. about 3.5% of the total EF of the four food products being manufactured at Merry Foods).

Figure 4. Proposed sustainable systems for the industry (Merry Foods)

4.2.1 Grid connected rooftop solar PV (GRSPV) system

As per the available roof area of the industry, the capacity of the proposed GRSPV system simulated by RETScreen software is 133 kW_p. The detailed specification of the proposed GRSPV system has been mentioned in Table 9. The proposed GRSPV system may annually generate 238,010 kWh of electricity for the climatic condition of Allahabad. It can meet 77.78% of the total annual grid electricity demand of the industry with a payback period of 4.6 years. The LCEF of the proposed GRSPV system has been estimated to be 56.18 gha and annual average EF of the system is 2.8 gha (i.e.,20 year life cycle of the system). The average life cycle ecological footprint per m² collector area of the solar PV system has been evaluated as 0.0694 gha/m².

Table 9. Detailed specifications of 133 kW capacity GRSPV system

Component	Specifications		EF(gha)
Multi -SI	Rated power at STC1: Module efficiency at STC2: Open circuit voltage (Voc): Short circuit current (Isc): Dimensions (l x wx h): Operating temperature range:	320 Wp 16.41 45.7 V 9.06 A 1970 mm x 990 mm x 35 mm – 40°C to 85°C	45.78
	No of panels:      416 Total panel area:    812 m^2 Embodied Energy:     2699–3120 MJ/m^2 panel area (Jungbluth, Stucki, and             Frischknecht 2009)
Frame(kg/6 PV Panel)	Mild steel: 7 ton Embodied energy of Steel:32.24 MJ/kg (Praseeda, Reddy, and Mani 2015)		7
Inverter	Capacity: 133 kVA Embodied emissions per kW capacity: 503 MJ (Pacca, Sivaraman, and Keoleian 2007)		1.3
Concrete	Cement: 60 packets (50 kg each) Aggregate (size 10 mm to 20 mm): 6.4 m^3 Sand: 3.2 m^3		0.64
Water	3.5 −5 litre per m^2 collective area (for cleaning and maintenance)		0.011
Transportation			0.77
Manpower	Skilled – 145 labour-days (ERRRET, 2017) Semiskilled- 625 labour-days (Economic Rate of Return for various Renewable Energy Technologies (ERRRET) report 2017) Unskilled – 120 labour-days (Economic Rate of Return for various Renewable Energy Technologies (ERRRET) report 2017)		0.68
Total EF of PV system			56.18
EF per kWp			0.42
EF per kWhe			2 x 10^−5

EF of grid electricity = 93.67 gha (i.e. about 78% of the total consumption)

EF of electricity generated by GRSPV = 2.8 gha

Total EF reduction = (93.67–2.8) gha = 90.87 gha

The proposed GRSPV system may reduce the annual ecological footprint of the food products by 3.2%.

4.2.2 Biogas plant

The proposed biogas plant capacity (4.8 m³) is based on the food waste (i.e. 80 kg/day, 11.5% of the total industrial food waste) which may annually generate7300 kg of biogas. (Ogur and Mbatia 2013).The detailed specification of the proposed biogas system has been mentioned in Table 10. The proposed biogas plant may completely meet the annual LPG requirement of the industry. The life-cycle EF of the proposed biogas plant has been estimated to be 1.33 gha and annual average EF of the system is 0.066 gha (20-year lifespan of the system).The total installation cost of the proposed biogas plant is about Rs. 120,000 with a three month payback period. The environmental impact of waste transportation and landfill may also reduce because 11.5% of the total food waste will be utilised in the proposed biogas plant.

Table 10. Biogas plant specifications

Materials	Unit	Quantity	Unit Cost (Rs)	Total Cost (Rs)	EF(gha)
Cement	kg	750 kg (15 packets; 50 kg each)	350/packet	5250	0.09
Gravel	ton	1.5	1250	1875	0.003
Brick	No.	200	7	1400	0.03
Steel Rod	kg	1550	55	85,250	1.03
PVC pipe	No.	2 pipes (20 ft. each)	900	1800	0.012
Rubber Pipe	m	20	50	1000	0.14
Manpower	labour-days	28	500/Skilled labour 300/Unskilled labour	11,200	0.025
Transportation	-	-	-	1181	0.00055
Sand	ton	4	1333	5332	0.000079
Plywood	kg	15	334	5000	0.001
EF of biogas plant					1.33
Total Cost of the biogas plant			119,288 (Assume transportation cost 1% of total cost of system)

^acost of materials are collected by the local market,

EF saving due to LPG replacement = 8.6 gha

EF saving due to waste utilisation = 11.5% of 0.464 gha = 0.053 gha

EF saving due to reduced waste transportation = 11.5% of 0.007 gha = 0.0008 gha

EF due to construction of biogas plant = 0.06 gha

Total EF reduction = (8.6+0.053+0.0008–0.06) gha = 8.59 gha

The proposed Biogas system may reduce the annual ecological footprint of the food products by 0.3%.

4.2.3 Roof-top rainwater harvesting (RRH) system

The proposed RRH system capacity (250 m³) is based on the annual water requirement of the industry and the annual rainfall received at Allahabad city. The proposed RRH system may also recharge the groundwater level.

The EF of the RRH system has been estimated to be 2.92 gha and annual average EF of the system is 0.146 gha (i.e.20 year life cycle of the system). The detailed specification of a rainwater harvesting system has been mentioned in Table 11.

Table 11. Rainwater harvesting system specifications

Component	Specifications (Kalimuthu 2016)	Materials Required				EF(gha)
Rainwater Potential	Annual Rainfall*Catchment AreaaRun-off Coefficient*1000	Roof Area = 1350 m^2; Annual Rainfall at Allahabad = 982 mm Run-off coefficient (concrete) = 0.6–0.8(Pacey and Cullis 1989))
Tank	V = taq V = Volume of the tank(litres); t = Length of dry season (days); q = Consumption,(litres per day) V = 250 days*1000 litre/days = 250,000 = 250 m^3	Material	Quantity	Total cost	EF(gha)	2.61
		Brick	6000 no	42,000	0.89
		Cement	5294.6 kg(106 pkt)	37,100	0.64
		Steel rod	1662 kg	8855	1.07
		Aggregate	8.5 m^3	17,850	0.00002
		Gravel	40 m^3	80,000	0.0005
		Sand	7.5 m^3	15,000	0.0002
		Concrete Mixer	10.1 hr	10.1 hr	600
		Vibrator	10.1 hr	400	0.001
PVC	4” PVC pipes, 20 feet in length	35 No.				0.181
Transportation						0.068
Manpower	68 labour- days	Skilled = Rs. 500/day Unskilled = Rs. 300/day				0.06
EF of RRH system						2.92
EF per m^3 rain water collected						1 x 10^−4

^acost of materials are collected by local market.

EF saving due to reducing water pumping = 0.29 gha

EF of RRH system = 0.146 gha

EF saving through RRH system = (0.29–0.146) gha= 0.144 gha (nearly half of the EF_w of the food products)

5. Conclusions

The environmental assessment of the food industry in India was performed because it is responsible for GHG emissions, waste generation and various types of resource consumption. In this study, the EF of the different food products manufactured in the Merry Foods was determined (i.e. 2884.44 gha for the year of 2017). The ecological footprint of pickle, jam, honey and chutney finished products are 1.67, 0.54, 15.94 and 2.18 gha per ton, respectively. The bio-productive land that had the maximum involvement for the food processing was found to be cropland (85.7%) followed by CO₂ absorption land (11.2%). The environmental impact of the packaging was estimated to be about 0.14 gha per ton of final food product.

This study also presents the net EF reduction that is feasible through the installation of sustainable technologies at Merry Foods; while also considering the EF of the sustainable systems as proposed. The suggested sustainable systems have the potential to significantly (3.5%) reduce the overall ecological footprint of the four food products manufactured at Merry Foods. The life cycle Ecological Footprint (LCEF) of 133 kW capacity grid connected rooftop solar PV (GRSPV) system has been estimated as 0.422 gha/kW_p while the EF of life-cycle electricity generation from SPV is about 2 x 10⁻⁵gha/kWh. The suggested biogas plant (digester capacity of 4.8 m³ with EF of the system as 1.33 gha) has the potential to reduce EF by 8.59 gha annually. Finally, the proposed rainwater harvesting system (300 m³ tank capacity with EF 2.92 gha) has the potential to completely meet all the water needs of the industry. Rather, the excess rainwater collected may replenish the groundwater table. If all the suggested measures are installed at Merry Foods, it may reduce the total EF of the food products by about 99.6 gha (i.e. about 3.5% of the total EF of the four food products being manufactured at Merry Foods).

In this study, the impact of the machinery used for food processing has not been incorporated, while environmental impact of the building has also not been assessed. So, the overall industrial ecological footprint could not be computed through this assessment. In the future studies, other sustainable systems, EF of the overall industry, and potential reduction through improved manufacturing techniques may also be examined.

The food processing industry is a land-intensive sector and therefore, the current study has examined the feasibility of reducing bio-productive land required in this sector through sustainable systems. In view of the projected growth in the food processing sector with rising population globally, this study is of considerable significance for various stakeholders. It is further suggested that EF labelling may be done on the packaging of food products, which can make the consumers and the manufacturers aware of the sustainability of their food products.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Dilawar Husain

Dilawar Husain is currently pursuing doctoral programme in the Department of Mechanical Engineering at the Motilal Nehru National Institute of Technology, Allahabad (UP), India. His academic interests include life cycle analysis, renewable system technologies and sustainability.

Pulkit Garg

Pulkit Garg is an undergraduate student of Food Technology and Management at National Institute of Technology Entrepreneurship and Management, India. His academic interest lie in the domain of agricultural sustainability and climate change.

Ravi Prakash

Dr Ravi Prakash is currently working as a Professor in the Department of Mechanical Engineering at the Motilal Nehru National Institute of Technology, Allahabad (UP), India. His academic interests include energy management, life cycle energy analysis, and sustainability. He is a Fellow of the Institution of Engineers (India) and a member of the International Solar Energy Society.