JSDEWES: Towards Sustainable Conventional Indian Houses: Linking Embodied Water-Energy Nexus to Pro-locals’ Architecture, Construction and Regulatory Reforms with Lifecycle Assessment and Scenarios Methods

The evolution of the study rests on the literature gaps of the domain exclusively explained in the following sections. The exploration is based on the hypothesis that ‘It is possible to achieve offsetting of EW & EE simultaneously in conventional houses through design decisions and policy insights that value the local strengths and construction practices.’ Such an approach is inevitable to have promising real-world applicability and a pragmatic nature.

The study aims to identify evidence-based & flexible-design decision-making, along with users & policy insights to offset EW & EE of conventional Indian houses. The study banks on the following objectives:

• To outline the best predictors (in materials and construction practices) comparatively in EE-EW nexus aspects from the site data.

• To align the best predictors and the locals’ interests to arrive at enough flexible solutions by involving local stakeholders.

• To tabulate design considerations and preliminary policy interventions vis-à-vis observations made and inputs of local experts, which also cater to sizeable societal diversity in income levels.

The study is a significant boost to the scientific knowledge bank in the following manner:

• Not only is the scant EW research contributed, but the outcomes’ base is also EE. The EWEE nexus is focused on seeking better sustainable building solutions.

• The experiment has an underlying pragmatic prerogative of local-centric insights, and its implications have enhanced real-world applicability and embraceability. Locals - both the houses’ owners and workers, local construction practices, their varied economic levels and corresponding material preferences are intervened.

• The study also stands out in not seeking alternate & non-native solutions vis-à-vis their fitment in onsite realities. Only the prevailing construction techniques are evaluated and tweaked towards goal-seeking, with the understanding that prevailing practices are locals-centric and are there to stay for a more extended period.

• Based on the EE-EW nexus, architecture plus design (A+D) decision-making through scenario manager application with local experts’ involvement makes the experiment notably novel. Never before were such realistic leads for varying kinds of the societal lot sought.

• The evolution is comprehensive in augmenting sustainable building practices. It meets all sustainability dimensions - environmental (EE-EW nexus), socio-cultural (locals’ preferences & native practices) and economical (varying economic levels of locals).

• Because the experiment is simplified and field-specific with commendable real-world application, it is inevitable to replicate it across regions of varying contexts to advance sustainable building practices further.

While the vitality of water resource availability due to the massive constructions was first examined by Australian researchers in 2004 [30], the growth in EW research remains below par. Australian, USA and Indian researchers have made few scholarly EW contributions in the last 20 years. However, the emphasis is still on sparking the researchers elsewhere. The first Indian EW study emerged in 2011 [31], and follow-up studies [32], [33] till 2022 show significant similarity in the methodology and system boundary limitations to the 2011 one. The rejuvenated approach for EW assessment in developing nations, explicitly considering the low monetary value associated with water, is argued in an in-depth Indian study [17]. A 2022 study [20] proposed a significantly improvised EW assessment framework, replicable for most developing countries and encouraged EW studies across contexts and regions. A USA research group has also developed a few EW studies [34], [35] in recent times using the stable and developed economy of the USA through a methodology better known as input-output analysis.

Auditing all the production process steps is almost impossible, which returns underestimation in the process-based lifecycle assessment method. Contrary to the bottom-up assessment involved in process-based methodology, the top-down assessment approach often overestimates consumption and is known as Input-output (I-O) methodology. Including some unnecessary sectors in the calculations explains the overestimation of the I-O method. For the Indian industry, economy-based I-O data are invariably unavailable. At the same time, the economy's instability is another question in the present world order vis-à-vis stable economies like the USA and Australia. Furthermore, the economic equivalency of water and energy consumption is incomparable in the field scenarios. So, an accurate picture of water consumption is difficult to achieve with the I-O method in the Indian context. Given the present EW data bank and the awareness, using hybrid methods or triangulation approaches is not a bar to carry the EW studies [28]. However, process-based methods following the bottom-up assessment are a fitting way to go in developing countries. The bottom-up approaches of EW [36] and EE [37] assessment were also observed for Indian constructions.

Most EW studies before 2022 focused on quantitative assessments as EW contemplations bear a toddler and exploring research status. However, few could foresee the EW linkage to energy at the building level and attempted EW-EE nexus studies. Proponents have recently outlined the carbon-energy-water nexus for building construction [11], [21]. Table 1 details the various construction EW studies that involved its nexus to EE and embodied carbon (EC).

Table 1 clearly illustrates that the USA’s researchers are more inclined to EW-EE nexus research but consistently use the I-O data of the USA’s developed economy. Indian studies, such as [21], [28], showcase the more reliable approach, which is suited to developing economies and process-based assessments following material inventories. Both USA and Indian results show uniformly in the EW-EE correlation, but in-depth findings at the material level were more detailed in Indian studies.

The positive and direct relationship between embodied energy and embodied carbon is evident in the building construction sector [21]. It explains the massive EE research [43] followed by net zero mission [44] and carbon-led sustainable programs of different nations [45]. A Chinese study advances to explore the energy-cement-carbon nexus vis-à-vis the prospective urbanisation in China [46]. The sustainable use of building materials in architecture and construction is very much in focus [47].

Table 1 also includes the solitary attempt of Australia [42], which involved the I-O data of the Australian economy. However, the correlation between EW-EE remains consistent with those of other global efforts. Through Table 1 studies, not only is the importance of EW outlined, but it has also been found that conserving EE measures over the decades is insufficient to have EW-conscious buildings [39]. Thus, EW-conservative buildings definitely demand fresh and innovative insights from building researchers, as Table 1 studies uniformly seek.

The water-specific focus remains out of consideration in most building-specific research and field contributions [13]. While the trade-offs between EE and EW have been predicted [48], the direct or indirect linkages between EW and EE need consolidation. Another limitation of the literature calls to advance the EW quantifications to the corresponding measures in the Architecture plus design (A+D) phase and policy-level decision-making. Indian EW studies of 2022 [20] and 2024 [28] relate quantifications to some extent to A+D measures. However, a concrete approach or evidence remains elusive. A recent study from the USA sees the linkage of building surface aspect ratio to resource consumption [29]. An Iranian research group established the EW assessment for building typologies based on various building elements [49]. Through a follow-up study, they outlined the sustainability of Iranian vernacular architecture based on water consumption parameters [50]. The fact that researchers see the worth even in assessing drinking water consumption by construction workers during the onsite construction phase [51] is worthy enough ground to contribute more and more EW studies. While proponents argued [40], [41] that EW conserving design measures would increase EE and viceversa, seeking EW & EE conscious design decisions is novel. No study has focused on the real-world applicability of EW-EE nexus-based design decisions, taking various local strengths and varying users’ economic levels as one size does not fit all. So, furthering design insights to user-level and policy-level actionable takeaways return invaluable takeaways towards 2030 SDGs and strengthen sustainability practices.

The study explores conventional houses in Jammu. The city is on course towards becoming another urban centre of modern India, specifically after the change of Jammu and Kashmir’s special legal status in 2019. Moreover, its location suits education, business, and safety vis-à-vis other parts of Jammu and Kashmir, a union territory in India.

The rising peripheral houses, built by the migrated population to Jammu, are primarily low-rise conventional houses. Rarely are the documented mixes and specifications followed in their constructions. Indeed, masons, in consultation with house owners, volunteer for the design and construction decisions of the houses. The conventional houses of Jammu have already proved high in EW [20] and EE [52] consumption. The consumption indexes are worse if the share of non-revenue water and energy and the water and energy supply leakages are also considered because data reveal a 30% leakage attributed to Jammu City’s water supply [19].

Table 2 outlines the pertinent details of the three houses, herein referred to as CJH-1, CJH-2, and CJH-3. These houses represent conventional construction in terms of construction technique, materials, location (type of construction personnel involved) and years of construction (uncontrollable factors like weather).

In principle, both ISO 14046 LCA [53] and water footprint network methodology are similar for water footprint assessment. The only difference lies in the dissimilar objectives of quantitative assessments and results interpretation. The current approach extends the same water footprint approach to the EW assessment and includes interpreting results. A similar chronology of assessment also exists for environmental management-based ISO 14044 LCA methodology [54]. Hence, following the joint preview of the EW- and EE-based assessment methods and generalised steps for LCA, the detailed study methodology is framed and illustrated in Figure 2. As the literature also shows, the current study only follows the verticals defined by the ISO frameworks, keeping in view both EE and EW assessments together, along with the scope and limitations. The impact categories are framed as common ones for EE and EW and range from outlining the consumptions at the unit level and aggregate level to further focusing on per unit construction area of the houses taken, as subsequent sections explain.

The highlighted shapes in Figure 2 indicate essential LCA verticals, i.e., goal and scope definition, inventory analysis, impact assessment, and results interpretation. Other shapes outline the primary operations under each vertical in chronological order, as followed in the study. After the goal and scope definition, EW and EE quantifications are carried out. Afterwards, the impacts are assessed in light of impacting materials, which leads to further inferential analysis through scenario-making exercises. Finally, various scenarios are framed and compared with the base case to deduce the pertinent insights.

The boundary conditions are chosen to understand the EW and EE impacts in the preconstruction phase, which comprises materials selection, A+D, and other preferential decisions influenced by economic affordability and policy impositions. So, the cradle-to-gate lifecycle phase of construction is covered. In contrast, the construction works, occupation, and demolition phases are kept out of scope, as shown in Figure 1, depicting conventional house construction’s various life cycle phases vis-à-vis EW and EE consumptions, also showing the study boundary and the different parameters involved for each of the life cycle phases. As the literature encourages, the various processes involved in the cradle-to-gate phase are holistically covered by consulting EWCs and EECs – but specifically from the Indian ones. The building furniture, furnishings, PVC conduits, and sanitary fixtures are excluded from the material inventory, while the inventory is up to the ready-to-move-in phase of the houses, including external plot development. The boundary is limited to 10 top impacting materials, as previous studies and local experience urge.

The research study uses the following tools and techniques:

• A generalised assessment chronological approach as provided by the ISO 14044 and ISO 14046 LCA frameworks.

• The scenario manager technique tweaks the prevalent material combinations and construction techniques to offset EW and EE simultaneously.

• Quota sampling technique, as the availability of material inventory to precision, is a vital dimension behind the final selection of the cases and is ensured through field investigations and consistent site visits.

• A cross-sectional technique allows the selection of the houses that are completed in all respects before data collection. It ensures that the selected houses bear a resemblance to uncontrollable factors like climate and simultaneously are not located at distant locations from each other. Besides being constructed in almost similar calendar years, they are also the typical representatives of the local conventional constructions.

• The approach is centred on sustainable development tools through locals’ participation to seek local-centric design decision-making & other regulatory interventions.

• Data charts and tornado plots are used for analysis and data interpretation.

• The assessments refer to the latest Indian databases for material-wise hybrid embodied coefficients.

The exploration is limited to the cradle-to-gate phase, only considering material quantities for EW and EE assessment. Energy and water use attributed to the complex bottom-up production steps is interpreted using embodied energy and water coefficients from the latest Indian literature. The analysis builds on three houses against the literature evidence of involving even one case in many studies. The extent of availability or devising accurate material consumption data (inventory) is the dominant criterion behind the selection of houses, besides these to be the ideal representative of conventional constructions of the region. The scope relies on accurate data availability, so only 10 materials are assessed, which are indeed the top-impacting EW or EE materials outlined in previous research. Moreover, the author’s familiarity with the local constructions helps to choose the selected materials and pertinent houses.

The study limits to the local expert’s assumptions-based material quantities in the scenarios because of invariably lesser use of documented mixes, specifications or technical inputs during the conventional constructions. Prospective studies using software for specification, design and policy-based iterations may yield slightly varying results. However, involving and valuing locals in decision-making nudges the sustainability approach. Scenarios involving alternate building materials are kept out of scope, as are the scenarios that intervene in the onsite construction phase. For devising the scenarios, the average of the three cases is taken as a base case rather than any case from the literature. With an understanding that there can be other scenarios, the experiment is limited to 10 pertinent scenarios with the available experts, time, and other resources for this study. Moreover, a lasting attempt is made to consider varying societal sections economically to assess and improvise the ongoing building regulations, policy reforms, people preferences and A+D practices. Given that the cost implications of the solutions still need to be verified, the study’s potential scenarios are devoid of its economic viability check, which can be considered a limitation.

The exercise relies on precise inventory availability with the site owners/contractors, which is seldom the case for conventional houses in Jammu. The survey exercise had to access 90-plus houses, predominantly those already completed, to arrive at the pertinent cases. Data quality and consistency were highly prioritised throughout the entire exercise. Only such cases were selected where the inventory was comprehensive and easy to relate to the physical construction for verification, and in addition, the contractors, masons, and house owners were approachable for queries on inventory formulations. Authors have also attempted an exercise to access the same houses and contractors involved by different sets of observers during the gap of six months from the initial selection of the houses. The uniform & satisfactory set of information collected in both instances only led to the final analysis as per the research design. Further, one building’s availability for many embodied energy- or water-related studies was an encouraging factor in selecting the three houses.

Another dimension behind the houses selected was the contribution of uncontrollable parameters related to the weather of the place. All the houses are located sufficiently close in the same region and constructed almost in the same calendar years, so the weather impacts on energy and water consumption are uniform in all cases. All the houses were ideal representatives of conventional construction. The materials, techniques and personnel involved are typical of the region vis-à-vis conventional house constructions.

Table 3 depicts the inventory for all the ten materials covered in the study for each disaggregated case, as collected by field investigations and onsite records available. The study follows the functional units for the material quantities as per the utilised database. The database values depict water consumption [kL] per unit material quantity [FU] consumed, herein specified as [kL/FU] and labelled as hybrid embodied water coefficients (EWC). As per literature, hybrid accounts for the contribution of all complex ‘direct’ and ‘indirect’ components involved in the production process (including upstream processes and humans involved), as Figure 1 shows and are computed by both the bottom-up and top-down LCA approaches (as applicable) to arrive at the final value.

Notably, the material inventory is available in conventional site units, which at times differ from the functional units (FUs) of the database values consulted for unit material consumptions. So, such values stand converted through standard conversion factors of Indian materials to arrive at the corresponding values in units in consonance with the FUs of hybrid EWC consulted from the databases, as shown in Table 3. For example, the cement inventory is available in bags number (50 kg each). Accordingly, units are transformed into desired ‘metric tons’ to suit the database units. However, steel (in ‘metric tons’) and brick (in ‘numbers’) units did not invite any conversion.

The EWC (α) values are sourced from notable recent Indian studies [20], [21], [28]. Besides the material-wise consumption for each house and the respective EWC, Table 3 also includes the total material consumption in aggregate case CJH-A, in the FUs at par with the FU of the database consulted.

Like Table 3, Table 4 illustrates the material-wise consumptions for the aggregate and disaggregated cases in similar units to the hybrid embodied energy coefficients (EEC) notated by ‘β’. The site data invariably return a uniform conventional unit employed locally. Still, to suit the scientific databases, these units are converted into database units using standard conversion factors for the Indian context. For example, the standard Indian ceramic burnt brick of size 230 mm × 115 mm × 75 mm, having a standard mass of 3.5 kg, is used in Jammu. Table 4 supports calculating the EE for a house or all the houses taken together (CJH-A). The process also efficiently evaluates material-wise EE consumption for each house and the aggregated case.

The following equations deduce the quantitative impacts of EW and EE for each house.

Where Q_j [FU] is the quantity of j-th material (among 10 materials) represented in functional units.

Accordingly, Table 5 details the EW of each disaggregated house and the aggregated case (CJH-A) in a couple of ways - material-wise EW consumption and total consumption EW considering all the materials together. Similarly, Table 6 showcases the material-wise EE consumption and total EE consumption for the disaggregated and aggregated cases.

Using total EE and EW consumptions of the aggregated and disaggregated cases from Table 5 and Table 6 and the respective total construction area, the consumptions are assessed per unit construction area basis. CJH-A represents the construction area of all the houses together. Figure 3 illustrates the EW details. The relationship between EW and the covered area is not prominent in the three cases. However, for the nearly 250% covered area increase from CJH-1 to CJH-2, the EW value only increased to 20.78 kL/m² from 18.75 kL/m² (10% only).

At the same time, CJH-1 and CJH-2 are almost similar in area but differ in EW by an equal margin of 10%. Several underlying reasons exist, including the difference in finishes, the number of RCC slabs (FAR and the ground coverage) and the number of rooms. Nevertheless, a coherent relationship between the covered area and EW has not surfaced, as is evident in Figure 3.

CJH-A’s EW is assessed at 19.73 kL/m². An Indian study finds 22.39 kL/m² for 27 materials taking 4 houses [20]. The first Indian EW study computed 25.60 kL/m² EW, but the very high EWC of steel considered in the study influenced the results, even though it considered only three top-used materials [31]. Another Indian study [17] covers 18 materials and finds 16.7 kL/m² EW under the joint impact of Indian and Australian [27] EWCs. A residential study from a water-scarce country finds only 3.34 kL/m², although it covered only three materials involving various typologies of different locations [49]. While the scope to conserve EW exists, quantitative comparisons do not make sense considering the above studies’ holistic preview. Moreover, developed countries like Australia involve more machinery in production, while the Indian material industry is heavily dependent on humans. So, the unaccountability towards water use (UFW) in India also accounts for more EW consumption with more humans involved. Examining the chronology of top impacting parameters like material use is more sensible, as literature [28] also upholds.

Figure 4 details the EE consumption vis-à-vis the construction area of the houses individually as well as the aggregated one (CJH-A). The total EE values in MJ are sourced from Table 6, in addition to the construction area of respective or aggregated houses.

CJH-2 is returning a lesser EE of 3547 MJ/m² compared to smaller residences, CJH-1 and CJH-2. As discussed, there are potential underlying reasons. EE per unit construction area in CJH-2 is reduced due to the distributed impact of high EE on the foundation, as CJH-2 is a two-floor construction. CJH-1 and CJH-3 involve single-storey construction only, while a greater number of walls and partitions accommodate the needs of a family similar in size to CJH-2. So, both the number of floors and walling material seem decisive. However, the EE vis-à-vis construction area pattern is far more apparent than the EW case.

CJH-A returns 3964 MJ/m² EE. As per the literature, a range of 3000 to 5000 MJ/m² stands outlined for the Indian context [58]. Other Indian studies find higher 7350 MJ/m² [59] and lesser 2092-4257 MJ/m² [60] EE values for different system boundaries and varied construction materials. A study using the native and Australian EEC observes it at 7158 MJ/m² [28] while evidencing nearly the exact chronology of top-impacting materials irrespective of the EECs used. Notwithstanding, prominent non-Indian studies [61]-[64] report a higher EE than Indian ones and translate that increased machine dependency on materials production overseas is counter-productive for EE regarding human-oriented Indian manufacturing. It leads to contemplating the impacts of the various materials on the EE or EW consumption per unit construction area. Then, it is feasible to have a material-to-material comparison concerning the respective percentage of EE or EW share in total consumption and work towards doing more with less, as SDG 11 encourages. The aggregate case (CJH-A) is only taken up as the representative of the cases analysed to see material-wise EW and EE impacts, thus compacting and simplifying the process,

Figure 5 shows the material-wise EW consumption in CJH-A (Table 5, last column) and the total CJH-A EW consumption of 11,688.6 kL, expressed as a percentage, thus clarifying the entire picture for the EW component. Sand and stone aggregates top the impacts, followed by plywood, paint, and toughened glass. The result agrees with the latest Indian EW study [17]. Steel, cement and brick receded from the contention concerning other materials, contrary to the findings of another Indian study [31], which did not consider other materials in the assessment. Thus, this study’s significant system boundary is refining the EW research. Similarly, Figure 6 depicts the percentage material-wise EE consumption for CJH-A. The values are derived using the quantities in Table 6. The material-wise EE behaviour is observed to have reversed in relation to the EW behaviour. Brick, cement and steel impacts top the list, while others sway away significantly. The results align with the various EE studies [16], [58], [65]. A comparison between water and energy indexes indicates that the material-wise impacts are more distributed for EW consumption (Figure 5). At the same time, more than half of the materials seem insignificant in the case of EE (Figure 6). It is inevitable to see a material-wise comparison between EE and EW.

Figure 7 illustrates the comparative percentage contribution of total materials in the aggregated case. All the materials are plotted along the horizontal axis. At the same time, the differentiated bars along the vertical axis show the percentage of material-wise EW and EE contributions for CJH-A.

Figure 7 is a clear testament to the differential behaviour of materials in EE and EW parameters. For example, brick is the topmost EE-impacting material. However, it retards significantly from the top EW-impacting material. The outcomes create a hefty task because the preference for brick use is enormous among locals. The prolonged EE research has already contained EE significantly, but the EW-conscious approach involving high brick use might neutralise the EE-offsetting. So, finding the ‘opportunity’ in the ‘threat’ of high EE-laden brick use can be one of the design-decision guides. Cement shows a similar behaviour, too. On the other hand, toughened glass, paint, and plywood have significant EW impacts but are satisfactory in EE impacts, as per Figure 7. Sand and stone aggregates (S_A) also have a similar pattern. Steel dominates more in the EE aspect than EW. Using different methodologies, the proponents [40], [41] also discovered the weak and inverse EW to EE relationship at the material level, even at different locations. So, the argument remains that the efforts for EE-conscious buildings over the decades do not cover the EW domain appreciably. Hence, the efforts towards holistic sustainable construction seem lacking. Building construction players must ensue for a sustainable building solution that simultaneously conserves EE and EW.

To practice sustainability, the involvement of stakeholders and, specifically, the locals is extensively advocated for real-world applicability and success. Thus, to seek EW and EE conserving building solutions, it is essential to seek the stakeholders’ choices first for optimum on-field implementation. The stakeholders, i.e. the building owners, are non-compromising towards a few aspects of the conventional houses while remaining flexible to fewer others. For this reason, this contribution only looks to intervene in conventional practices and not look for alternate solutions. Alternate solutions are potentially challenging for building owners to embrace owing to the prolongation of conventional practices, the availability of such materials/labour, and the ease of using and relating to them. However, not one but multiple materials and techniques are already in vogue for conventionally constructing various building elements. Hence, this exploration finds it more logical to assess the most appropriate existing construction method vis-à-vis EW or EE impacting one, indeed, with the evidence.

Building regulations in Jammu specify the ground coverage, setbacks and total construction area vis-à-vis the plot sizes of conventional houses. Conventional practices involved the preference towards wood-joinery (doors/window frames). However, its availability has whittled down its use. Mass boulders are the preferred way of constructing foundations. Most conventional house owners are flexible on finishes, structure systems (load bearing or composite or RCC frame), and foundations (mass boulders, stepped foundations in brick, or complete RCC foundations). Wood is not a preferred material in present times owing to cost, durability and availability in good quality. Plywood, toughened glass, and expansive paints are taking centre stage in the choices of contemporary house owners. It is worthwhile to mention that there is regular non-compliance with building regulations in most conventional houses, specifically in the city’s fringe areas. Monitoring and reporting building regulations are getting stricter with time, and this is a welcoming move towards the real-world implementation of this study’s purpose. However, all the choices above in building construction practices have a lot to do with the economic well-being of the house owner. For example, a middle-income group (MIG) house owner prefers 4-5 bedrooms despite a family size of 4 or 5. The high-income group (HIG) even surpasses that for the same family size and prefers the expansive finishes. At the same time, the lower income group (LIG) can afford only a bare minimum to start with his shelter and would add the rooms with time as per need and income growth. Literature finds evidence [66] of intervening in the building regulations for energy and water use; however, construction or material-specific modalities need deep regulatory insights. So, it is worth seeking insights and taking cognisance of the building bylaws and the owners’ preferred choices vis-à-vis the family’s economic status.

With the purpose of determining how future houses should be visioned to consume less EE and EW per unit construction area, a scenario creation exercise is taken up. Overall, the exercise tells us how using native methods and preferences can easily start offsetting EE and EW vis-à-vis current consumption as per the actual cases taken. Through detailed site visits of the authors involving discussions with 22 stakeholders of the study region, which involved four architects and several contractors/masons & house owners, various scenarios are devised to see their impacts on EE and EW. The scenario exercise is based on the observed impacts in previous sections vis-à-vis local building practices, locals’ interests & issues and building regulations. As a result, the scenario conditions and resultant material estimates are generated by the assumptions provided by the consulting stakeholders, which are the local construction experts. The authors’ familiarity with the conventional constructions and the preferences/challenges of the local community also benefit the entire exercise.

The aggregated case CJH-A is a base case in the scenario-building exercise. It is of utmost consideration in devising and shortlisting scenarios that most new constructions belong to LIG or MIG only, as this economic group is the principal constituent of Indian society. The scenarios devised are explained in Table 7, where the EW scenarios are abbreviated as SCJH-1 to 10 in addition to the ‘original’ scenario. The original scenario considers the material-wise consumptions in aggregated case (CJH-A) and corresponding EE and EW consumptions. Many other scenarios also emerged, but owing to significant unpragmatic considerations, they are done away with. While conditions for EE or EW scenarios are the same, EE scenarios are named for convenience by adding the suffix ‘E’ to the EW scenario names, as Table 7 shows.

Table 8 summarises the EW scenario manager summary as generated through the decisions of the survey with the local construction players. There are, in total, 11 scenarios (original plus 10) where all the material quantities are assumed on the set of pre-requisite conditions devised, as explained in Table 7. The larger purpose is to seek the best-fitting EW conserving scenario and re-assess the conditions for further onsite implementation through policy and onsite reforms. The vertical columns explain the scenarios, while the horizontal rows depict each scenario’s materials-wise EW quantity [kL]. The construction area of the aggregated case (C_Area) is also shown in a row towards the bottom half of the table. Concerning the inputs of material-wise EW quantities [kL] and C_Area [m²], the output in EW per unit construction area [kL/m²] is assessed using the scenario manager technique and reflected in the last row of Table 8. The scenario manager also provides the overall summary explaining the inputs and outputs of all the scenarios, as Table 8 illustrates. For better comprehension, all the inputs and outputs are demarcated in distinguished colours, showing their comparison, i.e., higher, equal or lesser to the original scenario values, as per the index provided at the bottom.

Like Table 8, Table 9 and 10 illustrate the inputs and outputs summary detail of EE scenarios, using the details of Table 7. In Table 8 and combined Tables 9 and 10, five scenarios return less EW than the base case with a minimum of 12.05 kL/m² for SCJH-6, corresponding to 39% EW offsetting. On the other hand, eight scenarios reflect conservation in EE compared to the CHJ-A, with the minimum being 2586.1 MJ/m², i.e., an EE saving of 35% (SCJH-9E). However, both the best-performing scenarios are different, and so are the worstperforming ones, too. It is an impending proof that:

• EW and EE are not positively or directly correlated with each other.

A few proponents [21], [34] also predict the inverse EW-EE relationship. Both SCJH-2 and SCJH-2E involve C_Area reduction by 20% but also return higher EW and EE than the ‘original’ case. Both scenarios require a reduction in every material. However, the corresponding decrease in C_Area (denominator) compensates for the reduction of the materials (numerator) and returns higher EW and EE per unit construction area. It implies:

• Area reduction alone cannot check EE and EW per unit construction area unless other concurrent measures exist.

So, it concludes that EE conservation measures over the decades have yet to sufficiently check EW in parallel, as suggested by a few preceding studies [34], [41]. A special effort is an eminent requirement to contain EE and EW simultaneously through EE-EW nexus studies on building construction. A comparative picture of the scenarios devised is plotted in Figure 8. All scenarios except the original are concisely abbreviated for the same convenience. For example, scenarios SCJH-1 or SCJH-1E are represented as one (1), and a similar approach for other scenarios is also taken. Figure 8 contains the original plus ten scenarios devised for the aggregated case (as already detailed in Table 7) along the horizontal axis. At the same time, EE [MJ/m²] and EW [kL/m²] are plotted along the primary and secondary vertical axes in the tornado plot. The distinction of the impacts using colours clarifies the meaning in Figure 8. As it reflects, the vertical bars are for EE while the graph line represents EW. The horizontal axis is shifted (red) to intercept the primary vertical axis EE reading of the original scenario (3964 MJ/m²). It simplifies scenario distinction, which returns more or less EE quantity than the original scenario plus the corresponding EW quantity from the secondary vertical axis in Figure 8.

This figure distinctively illustrates two scenarios (1 and 2) with more EE than the original scenario through the two bars (green) on the upper side of the shifted horizontal axis (red). Scenario 1 reports the highest EE but not the highest EW, while the lowest EW rests with scenario 6 (12.1 kL/m²) but does not return minimum EE, which strongly points to a nonrelationship between EW and EE. Similar reflections are also in scenarios 9 (minimum EE but not minimum EW) and 10 (highest EW but not highest EE). So, the non-positive relationship between EE and EW is potentially corroborated. However, among the eight scenarios on the lower side of the horizontal axis returning lesser EE than the original scenario, three scenarios (7, 9 and 10) still report higher EW vis-à-vis the original scenario. Such a reflection indicates the reverse behaviour of EE and EW in building construction, as also hinted by various proponents [21], [28], [39] before.

A few scenarios, like scenario 8, do keep the hopes alive that exceptions are possible with legible policy and A+D interventions. So, the interpretations of the comparative EE and EW scenarios (Figure 8) lead to the following:

• The best-performing EW scenario 6 reports 12.1 kL/m² EW in the aggregated case and conserves 39% vis-à-vis the original scenario. However, only a tiny fraction, 4.5%, of EE conservation is found; the reported EE result is 3786 MJ/m².

• The second-best-performing EW scenario 8 reports 14.3 kL/m² EW, a reduction of 27.5% vis-à-vis the original scenario. EE reduction is 27% compared to the ‘original’ with an EE report of 2905 MJ/m².

• Other EW scenarios like 4, 3, and 5 report much higher EE and EW values than scenario 8.

• The best-performing EE scenarios 9, 10, and 7 report only a fraction of EE offsetting compared to scenario 8, in addition to considerably escalating EW.

Observing also that scenarios 3, 4, 5, 6, and 8 return less EW and EE than the original, it is essential to highlight the corresponding architecture plus design (A+D) and policy interventions devised. Indeed, the said scenarios uphold the hypothesis of the study. The decision-making at various stakeholder levels must be outlined to consolidate the evidence further.