Italian Consumers’ Awareness of Climate Change and Willingness to Pay for Climate-Smart Food Products

Abstract

Understanding climate change awareness and its related risks is crucial to plan efficient climate-smart strategies. An online survey was conducted on Italian consumers with the aim to understand consumers’ inclination toward food products obtained with climate-smart strategies. Specifically, consumers’ awareness about climate change and willingness to choose and pay for products derived from climate-smart agriculture were investigated. Results highlighted two targeted consumers, one more interested in economic issues and more “conservative” and the second one more concerned with climate changes risks with a higher interest in environmental and ethical values (fair trade), representing the primary target consumers for climate-smart foods. This segmentation can be useful to identify expectations and purchase drivers that can facilitate climate-smart policies and the establishment of the climate-smart foods on the market.

1. Introduction

Due to its direct impact on agricultural activities, climate change (CC) is one of the most significant challenges that European agriculture has been facing so far.

Even if, from 1990 to 2018, Europe reduced 21% of its CO₂-equivalent emissions, the agricultural sector would account for about 10% of the EU GHG emissions [1], most of which are due to animal production. In Europe, every year, the dairy and beef sectors are responsible, respectively, for 195 and 192 Tg of CO₂-eq [2], mainly in the form of non-CO₂ compounds: primarily N₂O emission, largely due to fertilizer application and grazing, and CH₄, mainly from enteric fermentation. In addition, intensive production requires increased agricultural inputs such as fertilizers, water, chemicals and energy, leading to other negative environmental impacts such as soil degradation, water eutrophication and reduced biodiversity [3].

Concerns about the impacts of climate change are growing among organizations and governments around the world, as is consumers’ attention to environment-friendly production [4,5,6].

The Common Agricultural Policy examined and developed support programs in order to integrate climate change adaptation and mitigation strategies into EU agriculture [7]. However, an approach purely based on financial incentives to ensure the transition to climate-smart and sustainable agriculture is not enough; agriculture must be supported also by ethical consumption and conscious behavior on the consumers’ side.

Climate-smart agriculture strategies are measures that can improve crop yields and turn farmland into carbon sinks, rethinking global and local supply chains to be more sustainable [8]. Food produced through climate-smart approaches (climate-smart foods—CSFs) could contribute to water and energy saving and lower CO₂ emissions, while promoting resilient agriculture to climate change issues (drought, extreme events, soil deterioration, exasperation of pests and diseases, deterioration of livestock conditions) [9].

CSFs can potentially achieve a well-defined market positioning by exploiting the attractive benefit offered by the “climate factor”. Indeed, it is known that “green marketing strategies” take advantage of “green advertising” to drive consumer’ choices toward sustainable buying behaviors [10]. Consumer science has been recognized to drive decision making in society’s challenging issues where good properties and intangible services or values (economic, ethical and environment) are closely connected [11]. Research based on the knowledge–attitude–behavior model (KAB) and consumers’ willingness to pay (WTP), conducted in several European Countries, focused on sustainability in general, while little information exists on “fighting climate change” as an effective driver of consumer choice. Korkala et al., 2014 [12], found that awareness of climate change leads to increased consumption of climate-friendly food in Finnish consumers; furthermore, other studies in Finland [13] showed that the perceived seriousness of climate change consequences seems to be a driver for climate-friendly food choices among social science University students.

Understanding the public perception of climate change is fundamental for defining the socio-political and economical scenery where policymakers and scientists operate [14]. Since concerns about environmental issues affect food choices and habits [15], stakeholders are interested in assessing people’s perception and awareness of climate change and associated risks [16,17]. The effects of climate change are uneven between different biogeographical regions [18], and the way they are perceived can also greatly vary from country to country according to socio-demographic characteristics, geography, perceived well-being and beliefs [19]; therefore, it is very important to monitor consumer awareness and perception of risks in different countries.

The success of sustainability-directed marketing strategies relies on consumers’ uptake of green products, since WTP can be a barrier to purchasing products; thus, its study becomes a key factor to highlight marketing actions to alleviate this barrier [20].

Several studies have focused on the importance of CC awareness [16,17,21], but there is no evidence regarding WTP’s influence on CSFs’ success.

This research focused on CSFs as an instrument to address policies to counteract climate change. The objective was to explore Italian consumers’ awareness of climate change and assess their intention to buy CSFs in order to plan strategies encouraging Italian consumers’ knowledge, appreciation and willingness to buy CSFs.

2. Consumer Awareness on Climate Change: A Brief Literature Review

Climate change has generally been perceived as a higher risk in developing countries than in most of the Western world [22]. So far, concern on CC has always been lower in the US and China than in continental Europe [23,24]. Indeed, in the UK and Europe the issue has consistently been perceived as “very serious” by public opinion [25,26]. A study conducted by the European commission revealed that the most feared risk was related to the temperature rise and to the consequent lack of water [27]. Europeans have expressed an increasing awareness of CC and are changing their eating habits to act in fighting its impacts [28].

In Italy, there is the highest percentage of citizens (81%) considering CC a global emergency requiring action, as assessed by the cross-county survey of the UNDP program in 2021 [29]. Current studies have highlighted Italians’ will to adopt mitigation strategies to counteract CC [28,30,31,32]. Recent research by Antronico [21] analyzed the socio-demographic, economic and education constraints which affect CC perception in Italy. Maricinoni [33] emphasized the importance of awareness campaigns in the hazard risk perception of CC. A study carried out by Vollaro [32] showed Italians’ consciousness of climate change events and a common consensus on the need to improve specific actions to manage CC impacts.

Italians are the most informed on climate change and its health-related impact [29] and can be more prepared to modify their lifestyle and eating habits [28] by introducing CSFs in their diets.

3. Materials and Methods

3.1. Survey Measures

An online questionnaire was created and submitted through Google Forms to probe the consumers’ knowledge about climate change and inclination toward CSFs.

Data collection was carried out in July–October 2018. Italian adults aged between 18 and 70 were recruited, through personal e-mail, among a list of 2276 consumers (1232 females, 1040 males), selected by IBE-CNR in the last few years, interested in food related research and available to answer online questionnaires.

All the participants answered the questionnaire voluntarily; they were informed of the main research outcomes and gave consent for their data to be used. Participation in the research was voluntary, and the right to privacy and data protection was respected in accordance with current legislation (GDPR 2016/679). Measurement items were adapted from the literature where possible. The wordings were revised to fit the context of this research [21,34]. The questionnaire was structured into different sections. Section 1 included socio-demographic information. Participants were requested to indicate sex, age class (age 1: <30 years; age 2: 31–40; age 3: 41–50; age 4: 51–60; age 5: over 60) and NUTS [35] region of residence.

Moreover, willingness-to-pay (WTP) for CSFs was assessed on a 0 to >8% range in order to define four WTP classes: Low, ≤3% (L); Medium, 5% (M); High, 8% (H); Very High, >8% (HH). WTP was also used to investigate the motivations to buy CSF (climate-smart food) and the benefit of a specific climate-smart Label. The measurement of WTP was adapted from Zhang [34].

Section 2 gained information on consumers’ inclination toward climate change through five climate change topics (

Table 1. Overview of questionnaire Section 2: consumers’ perception of CC impact and its mitigation actions.

Question	Class	Value 1	Value 4	Value 7
Perception of CC increasing	Late years	1	4	7
	Long time
	Future
	Never
How do you consider important this event’s impact on the environment?	Draught	Not important	Medium	High
	Extreme hot/cold/events	at all	importance	importance
	Pollution increase
	New pests
	Waste increase
	Land degradation
Define the efficacy of the following actions against CC	Policymakers’ intervention	Not effective	Medium	High
	Research on CC		effective	effective
	Economic support
	Consumers support
	Protect local productions
	Apply taxes against waste
	No action is very effective
	No action is possible
Define the research role in counteracting CC impact on the agriculture among the following	Eliminate CC cause	Strongly	Neither agree	Strongly
	Improve plant genotypes	disagree	nor disagree	agree
	Support local products
	Propose old varieties
	Improve systems of climatic alert
	Improve consumers’ education
	Promote organic agriculture
	Support vegetarian diets
	Support environment-friendly genetically modified organism (GMO)
Define what are the best “climate smart” actions against CC among the followings	Water saving	Not	Medium	Highly
	Promote adequate crops	encouraged	encouraged	encouraged
	Promote traditional crops
	Promote organic agriculture
	Encourage 0 km products
	Define climate-smart disciplines
	Encourage precision agriculture
	Lowering CO2 emissions
	Promote energy saving

). Questions were structured with multiple statements. All the measures were adapted from established scales and were quantified on Likert scales [20].

The Section 3 probed consumers’ inclination towards CSFs and a specific CS label. The seven-point Likert scale was employed to define agreement/disagreement on the following statements about CS label usefulness: “useful if provide a specific guarantee”, “useful even with added cost”, “useful without costs”, “useful to encourage consumers”, “useful to encourage producers”, “useful if supports exportation”, “useful if limits importation” and “it’s not useful”.

Finally, a CATA Questionnaire [36] allowed the respondents to indicate the motivations to pay extra for CSFs among the following options: a guarantee of environment-friendly agriculture, more information on CSF, a guarantee of origin, a guarantee of low emission production, a guarantee of water-saving production, indication of fair agriculture, low price and a specific CS label.

3.2. Statistical Analysis

Statistical data analysis was performed with the R Software package, version 4.1.2, and descriptive statistics were used for socio-demographic data. One-way ANOVA was used to determine the main effect of gender, age, region of residence and WTP. Differences were considered significant when p < 0.05. A chi-square test was performed to measure the association between motivations to pay for CSFs and was considered significant when p < 0.05.

4. Results

4.1. Profile of Participants

Characteristics of the sample analyzed are summarized in

Table 2. Overview of participants according to sex, class age and NUTS region of residence.

Category	Class	Male	Female	Total
Age	Age 1	59.4	40.6	12.6
	Age 2	36.5	63.5	19.1
	Age 3	46.0	54.0	25.1
	Age 4	50.9	49.1	29.1
	Age 5	66.7	33.3	12.1
NUTS region	Northeast	47.7	52.3	44.7
	Northwest	41.9	58.1	17.0
	Center	57.1	42.9	20.5
	South + Islands	52.6	47.4	17.8

.

The consumer survey was answered by 546 participants: 50.5% female (n = 276) and 49.5% male (n = 270); the age classes were distributed as follows: age 1, 12.6%; age 2, 19.1%; age 3, 25.1%; age 4, 29.1%; and age 5, 12.1% of the participants. Most of the respondents (44.7%) were from the Northeast of Italy; 17.0% of them were from the Northwest, 20.5% were from the center, and 17.8% were from South Italy and the Islands.

The WTP classes (Figure 1) were composed of 34.8% of L respondents; 29.2% of M; 15.8% of H; and 20.2% of HH respondents. HH and L classes featured, respectively, 51.5% and 53.4% of males, while the H class showed a prevalence of females (52.6%).

4.2. Awareness of Climate Change

The highest agreement was registered for the statement declaring a higher CC impact in the late years, followed by the one suggesting CC being present for a long time. A low agreement was recorded for the statements declaring: CC is yet not present, CC will show up in future years and CC will never appear (Figure 2).

4.3. CC Expected Effects on the Environment

The most likely CC effects indicated by participants were drought and extremely hot weather, with the WTP-HH class being more aware of the risks than WTP-L. In addition to drought, WTP-L was less worried about extreme hot events, pollution increase and land degradation. Females gave more importance to most of the proposed effects, such as pollution increase, the emergence of new pests and diseases, land degradation, waste increase and extremely cold weather conditions. Slight differences were highlighted among different age classes and NUTS regions (

Table 3. One-way ANOVA model on the importance of the risks related to CC. Tuckey’s post hoc test was used to test the difference between different types of impacts (“all” column) and between different groups of the same independent factor (gender, age, NUTS region and WTP). Means with different letters correspond to statistical differences (“S” column; ns, not significant; * p < 0.05; ** p < 0.01; *** p < 0.0001). NUTS: Nomenclature of territorial units for statistics. WTP: Willingness to pay. WTP classes: Low (L); Medium (M); High(H); Very High (HH).

Impacts	All	Gender			Age Groups						NUTS Region					WTP
		F	M	S	Age 1	Age 2	Age 3	Age 4	Age 5	S	NE	NW	Center	South + Islands	S	L	M	H	HH	S
Drought	6.22 a	6.25	6.16	ns	6.35	6.17	6.39	6.19	5.89	ns	6.14	6.05	6.40	6.36	ns	6.01 b	6.41 a	6.37 ab	6.44 a	**
Extreme hot	6.04 ab	6.11	5.97	ns	5.90	6.04	6.13	6.12	5.77	ns	6.08	5.95	6.06	5.98	ns	5.79 b	6.13 ab	6.11 ab	6.34 a	**
Extreme events	5.97 ab	6.04	5.90	ns	5.67	5.88	6.15	6.08	5.79	ns	5.90	5.85	6.22	5.97	ns	5.84	5.99	6.24	6.14	ns
Pollution increase	5.86 b	6.16	5.55	***	5.94	6.03	5.90	5.77	5.67	ns	5.78	5.89	5.99	5.86	ns	5.56 b	5.92 ab	6.05 ab	6.17 a	**
New pests	5.44 c	5.57	5.32	*	5.14	5.47	5.56	5.45	5.42	ns	5.32	5.49	5.71	5.37	ns	5.25	5.44	5.64	5.62	ns
Land degradation	5.35 c	5.49	5.20	*	5.13	5.43	5.41	5.40	5.20	ns	5.19 b	5.29 ab	5.71 a	5.35 ab	*	5.16	5.24 ab	5.67 ab	5.71 a	**
Waste augment	5.30 c	5.61	4.99	***	5.54	5.59	5.32	5.03	5.21	ns	5.13	5.27	5.46	5.55	ns	5.19	5.10	5.62	5.50	ns
Extreme cold	3.82 d	4.14	3.49	***	4.14 ab	4.25 a	3.80 ab	3.52 b	3.56 ab	**	3.65 b	3.90 ab	3.76 ab	4.21 a	*	3.69	3.87	4.08	3.94	ns

).

4.4. Best Policies to Mitigate CC Effects

According to participants, the key contribution to fight CC impacts should come from scientists and policymakers, as confirmed by the WTP classes HH, H and M, while WTP-L was shown not to trust them. Males expressed a higher appreciation for policy supporting consumers, for CSFs economic support and for protecting local production compared to females. Young people (ages 1 and 2) trust research less and were more favorable to economic support for CS productions compared to older people. No differences were recorded as related to the region of residence. Overall, participants showed trust in mitigation policies against CC, as confirmed by the low agreement recorded for the options “no” or “not very effective” (

Table 4. One-way ANOVA model on the efficacy of public measure to cope with CC. Tuckey’s post hoc test was used to test the difference between different policies (“all” column) and between groups of the same independent factor (gender, age, NUTS region and WTP). Means with different letters correspond to statistical differences (“S” column; ns, not significant; (*) p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.0001). NUTS: Nomenclature of territorial units for statistics. WTP: Willingness to pay. WTP classes: Low (L); Medium (M); High(H); Very High (HH).

Policies		Gender			Age Groups						NUTS Region					WTP
	All	F	M	S	Age 1	Age 2	Age 3	Age 4	Age 5	S	NE	NW	Center	South + Islands	S	L	M	H	HH	S
Research	6.24 a	6.18	6.30	ns	5.90 b	6.11 ab	6.37 a	6.38 a	6.20 ab	*	6.11	6.27	6.26	6.49	(*)	6.01 b	6.36 ab	6.39 ab	6.48 a	**
Policymakers	6.18 a	6.13	6.22	ns	5.93	6.25	6.31	6.23	5.94	ns	6.08	6.12	6.44	6.18	(*)	5.91 b	6.31 a	6.36 a	6.49 a	***
Support consumers	5.83 b	5.67	5.99	**	5.88	5.95	5.93	5.75	5.59	ns	5.77	5.96	5.99	5.66	ns	5.68	5.90	5.95	6.01	ns
Economic support	5.26 c	5.02	5.50	***	5.58 a	5.52 a	5.09 ab	5.30 ab	4.82 b	**	5.25	5.23	5.25	5.32	ns	5.13 b	5.21 ab	5.75 a	5.30 ab	*
Taxes waste	5.22 c	5.27	5.16	ns	5.32	5.19	5.36	5.16	4.98	ns	5.11	5.61	5.20	5.14	ns	4.87 b	5.27 ab	5.32 ab	5.58 a	**
Protect local productions	4.92 d	4.71	5.14	**	4.77	5.24	5.09	4.65	4.89	ns	4.71	5.14	5.12	5.04	(*)	4.90	4.86	5.21	4.91	ns
None	2.98 e	3.07	2.84	ns	3.09	3.01	2.70	2.97	3.24	ns	2.81	2.88	3.02	3.35	ns	2.77	2.99	3.39	3.07	ns
Not very effective	1.87 f	1.96	1.79	ns	2.19 a	1.70 ab	1.69 b	1.94 ab	2.05 ab	*	1.87	1.91	1.67	2.11	(*)	1.87	1.96	1.91	1.59	ns

), with the young people (age class 1) being the most skeptical about them.

4.5. Importance of Scientific Research about Mitigation Strategies

Participants indicated consumer education as the most important research aim, followed by research supporting local production (

Table 5. One-way ANOVA model on the importance of research activities for coping with CC. Tuckey’s post hoc test was used to test the difference between different research roles (“all” column) and between different groups of the same independent factor (gender, age, NUTS region and WTP). Means with different letters correspond to statistical differences (“S” column; ns, not significant; (*) p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.0001). NUTS: Nomenclature of territorial units for statistics. WTP: Willingness to pay. WTP classes: Low (L); Medium (M); High(H); Very High (HH).

Research role		Gender			Age Groups						NUTS Region					WTP
	All	F	M	S	Age 1	Age 2	Age 3	Age 4	Age 5	S	NE	NW	Center	South + Islands	S	L	M	H	HH	S
Consumer education	6.29 a	6.39	6.19	*	6.17	6.37	6.41	6.23	6.18	ns	6.15	6.34	6.47	6.37	(*)	6.02 b	6.38 a	6.47 a	6.46 a	**
Local products	5.79 b	5.97	5.60	**	5.70	5.93	5.82	5.69	5.74	ns	5.65	5.81	5.96	5.88	ns	5.60	5.85	6.09	5.78	(*)
Climatic alert	5.71 bc	5.79	5.63	ns	5.41 b	5.46 ab	5.87 a	5.76 a	5.89 a	*	5.49 b	5.85 ab	6.06 a	5.72 ab	**	5.55	5.80	5.84	5.85	ns
Improve genotypes	5.55 bc	5.44	5.66	(*)	5.36 ab	5.10 b	5.69 a	5.70 a	5.79 a	**	5.41	5.49	5.83	5.66	(*)	5.47	5.68	5.50	5.70	ns
Eliminate GC causes	5.55 bc	5.54	5.55	ns	5.29	5.35	5.50	5.55	5.48	ns	5.45	5.59	5.55	5.74	ns	5.26	5.41	5.78	5.66	(*)
Support vegetarian	5.44 cd	5.51	5.36	ns	5.25	5.47	5.35	5.55	5.47	ns	5.25 b	5.26 b	5.84 a	5.61 ab	**	5.16	5.47	5.59	5.66	(*)
Organic cultivation	5.11 d	5.25	4.97	(*)	4.88	5.42	5.26	4.87	5.00	(*)	5.00	5.10	5.29	5.19	ns	4.75 b	5.18 ab	5.28 ab	5.59 a	**
Old varieties	4.60 e	4.93	4.27	***	4.41	4.77	4.73	4.51	4.61	ns	4.48	4.72	4.71	4.69	ns	4.59	4.52	4.80	4.70	ns
OGM	3.88 f	3.79	3.97	ns	4.36	3.57	3.78	3.98	3.90	ns	3.87	4.02	3.80	3.91	ns	3.75	3.96	4.13	3.81	ns

). Females attributed more importance to those issues together with the interest in the research investigating the old varieties. On the contrary, the WTP-L class gave the lowest importance to consumer education and organic agriculture. As for the age groups, older people showed higher interest in research providing climatic alerts and improving genotypes.

4.6. Best Practices for Coping with CC

Respondents indicated that reducing CO₂ emissions and saving water and energy would be the best practices against CC (

Table 6. One-way ANOVA model on the best practices to cope with CC. Tuckey’s post hoc test was used to test the difference between different practices (“all” column) and between different groups of the same independent factor (gender, age, NUTS region and WTP). Means with different letters correspond to statistical differences (“S” column; ns, not significant; (*) p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.0001). NUTS: Nomenclature of territorial units for statistics. WTP: Willingness to pay. WTP classes: Low (L); Medium (M); High(H); Very High (HH).

Best practice		Gender			Age Groups						NUTS Region					WTP
	All	F	M	S	Age 1	Age 2	Age 3	Age 4	Age 5	S	NE	NW	Center	South + Islands	S	L	M	H	HH	S
CO2 emission decrease	6.45 a	6.43	6.46	ns	6.20 b	6.42 ab	6.73 a	6.38 ab	6.29 ab	*	6.38	6.34	6.59	6.54	ns	6.18 b	6.47 ab	6.50 ab	6.71 a	*
Water saving	6.41 a	6.42	6.39	ns	6.08 b	6.39 ab	6.66 a	6.41 ab	6.22 ab	(*)	6.30 ab	6.18 b	6.69 a	6.53 ab	*	6.22 b	6.39 ab	6.49 ab	6.72 a	(*)
Energy saving	6.34 a	6.34	6.32	ns	6.00 b	6.46 ab	6.69 a	6.10 b	6.28 ab	***	6.20	6.25	6.53	6.47	ns	6.12 b	6.33 ab	6.53 ab	6.61 a	*
0 KM	5.71 b	5.88	5.50	**	5.36 b	6.04 a	5.82 ab	5.59 ab	5.47 ab	*	5.56	5.53	5.92	5.92	(*)	5.43 b	5.63 ab	6.03 a	5.96 a	*
Adequate crops	5.70 b	5.69	5.68	ns	5.54 ab	5.35 b	5.77 ab	5.92 a	5.63 ab	*	5.51 b	5.63 ab	6.05 a	5.74 ab	*	5.66	5.60	5.81	5.82	ns
CS disciplines	5.61 b	5.73	5.47	(*)	5.26	5.33	5.83	5.65	5.78	(*)	5.45	5.45	5.87	5.82	(*)	5.45 b	5.77 ab	5.88 ab	5.97 a	*
Precision agriculture	5.23 c	4.94	5.50	**	4.95 ab	4.67 b	5.44 a	5.44 a	5.30 ab	*	4.98	5.29	5.46	5.39	ns	5.21	5.31	5.47	5.38	ns
Organic agriculture	4.98 cd	5.14	4.75	**	4.62 b	5.19 ab	5.26 a	4.76 ab	4.76 ab	*	4.86	4.77	5.24	4.98	ns	4.64 b	4.98 ab	4.82 b	5.55 a	***
Traditional crops	4.92 d	5.11	4.72	*	4.60	4.92	5.06	4.85	5.11	ns	4.68 b	4.92 ab	5.06 ab	5.39 a	*	4.96	4.93	5.09	5.14	ns

). Females gave more importance to Km 0 and organic agriculture than males, trusting the perspective of precision agriculture. The WTP-L class gave low importance to most of the practices, compared to other WTP groups, in particular to organic agriculture. As for the age group, people of medium age (Age 3) were in line with most of the respondents, relying also on precision and organic agriculture. Young people (Age 1) showed distrust in most of the practices. Slight differences were registered among different NUTS regions.

4.7. Motivations to Buy CSFs

The most relevant driver for choosing CSFs was the guarantee of environment-friendly agriculture, followed by the information to consumers, the guarantee of origin, low CO₂ emissions and water saving. The last two motivations, together with the indication of fair agriculture, are considered less important, especially for WTP-HH. On the contrary, WTP-L gave higher importance to product cost, comparable to information, origin and water saving, even higher than low CO₂ (

Table 7. Motivation to buy CSF, for the total population and compared for WTP classes. X-squared = 47.051, df = 24, p-value = 0.003303 (ns, not significant; (*) p < 0.10; * p < 0.05; *** p < 0.0001) value = 0.9901. WTP: Willingness to pay. WTP classes: Low (L); Medium (M); High (H); Very High (HH).

Drivers	Total (n = 500)	HH (n = 101)	H (n = 79)	M (n = 146)	L (n = 174)
Environment care (*)	82.2	83.2	73.4	78.1	69.5
Information ns	67.8	64.4	65.8	67.8	56.9
Origin ns	63.6	56.4	58.2	58.9	57.5
Low CO2 *	63.2	69.3	59.5	58.9	51.1
Water saving *	62.0	70.3	51.9	53.4	55.7
Fair agriculture ***	51.0	60.4	53.2	47.3	35.6
Cost ***	40.6	13.9	38.0	26.0	56.9
CS label ns	26.4	25.7	26.6	24.7	26.4

).

4.8. Utility of a Specific Climate-Smart Label

Overall, respondents recognized a specific CSF label as useful if providing a specific guarantee and encouraging consumer and producer choices (

Table 8. One-way ANOVA model on the usefulness of a specific CS label. Tuckey’s post hoc test was used to test the difference between different labels (“all” column) and between different WTP classes. Means with different letters correspond to statistical differences (p < 0.05). WTP: Willingness to pay. WTP classes: Low (L); Medium (M); High (H); Very High (HH).

Label	WTP
	All	L	M	H	HH
Specific guarantee	5.84	5.32 b	6.03 a	6.07 a	6.02 a
Encourage consumers	5.13	4.67 b	5.22 a	5.26 a	5.72 a
Producers	5.05	4.54 b	5.16 a	5.37 a	5.66 a
No costs	4.28	4.50	4.05	4.43	3.99
With added costs	4.01	3.17 c	4.40 b	3.97 b	5.02 a
Support export	3.34	3.43	3.26	3.49	3.11
Reduce import	3.07	3.25	2.87	3.28	2.71
No importance	2.91	3.08	2.88	2.74	2.61

). This finding agreed with what was indicated by WTP M, H and HH classes that showed more attention to those aspects, with HH being the most favorable, even if the label would increase the cost. The WTP-L class was shown to be the least interested in the utilities, particularly if the label would increase the cost.

5. Discussion

Scientists have been investigating CC awareness, since a great public perception of the phenomenon favors the activation of policies to transform consumers’ behavior [21]. This study showed a clear awareness of CC in Italy, whose impacts became more evident in the last few years but, according to consumers, were present for a long time. Indeed, local perceptions have been found to be well correlated to real-world and tangible concerns [37]. These results are in accordance with Beltrame [38], describing Italian citizens as largely aware of the seriousness of the issue. The Italian public perception of CC relies mainly on information provided by mass media, which emphasizes the practical consequences of CC [38].

Despite relevant climatic differences among Italian regions, there was a slight influence of the geographic area on the study results. Indeed, CC perception is influenced by several factors that may be country and culture-specific [19]. Thus, the public belief in CC was more likely to differ in geographically, economically and culturally diverse areas across the planet.

The most feared risks were related to drought, hot weather and extreme events. This is not surprising since temperature rise and desertification are often reported as the first CC images by mass media [21]. Moreover, CC perception in Italy is mainly related to direct experience of anomalies [38]; thus, extreme events are another image which common sense associates with CC.

Women were shown to be more sensitive to several CC risks compared to men, indicating a higher awareness of developing threats, as confirmed by Korkala [12] and Zainulbhai [39], which highlighted women’s higher concerns regarding CC potential harms. Those findings are in line with women’s attention to health and a healthy lifestyle [40].

Consumers’ willingness to pay indicates the maximum price the consumers intend to pay for a certain product [41]. Research has highlighted that environmentally conscious consumers were inclined to pay more for green products [42]. In our study, the WTP segmentation was effective in identifying the targeted consumer profile interested in CSFs. The WPT-HH class appeared more aware of the risks than WTP-L. As highlighted by Li [43], people more concerned about the adverse effect of CC showed higher WTP for environment-friendly products. Slight differences among age classes and NUTS regions were recorded, suggesting that climate change awareness is not influenced by age and Italian geographic area of residence. Indeed, as highlighted by Firebaugh [44], age may not play a relevant role in CC belief and fear.

Overall, respondents showed trust in CC mitigation policies and positive feedback on the role of scientists and policymakers in counteracting CC impacts. Young people and WTP-L class appeared to have fewer expectations about research while being more positive about an economic intervention to support innovative CS productions, together with male respondents. Indeed, young people were shown to be more confused about climate change solutions and causes, which required specific scientific knowledge to be understood. Moreover, they are influenced by information sources such as TV and social media, which emphasize disastrous events without giving any clues on solutions and causes that remain difficult to understand for a young audience [45], feeding research mistrust.

The WTP classification was also effective in the issue regarding the most valuable research aim, which was indicated as considering consumer information and supporting local productions.

The L class gave less importance to consumer education and organic agriculture and, as expected, appeared to be more interested in economic issues and more “conservative”, giving low importance to education and organic agriculture. Indeed, as shown by Achterberg [46], distrust in science is associated with low education level, and as confirmed by age, older people, compared to young having a lower education level, showed higher interest in research providing climatic alerts and improving genotypes.

Participants showed a good capacity for discriminating agricultural practices effective for mitigating CC effects, indicating low CO₂ emission and water and energy saving as the most important practices for coping with CC. Indeed, conventional mitigation technologies, focused on CO₂ emissions reduction, being the first Kyoto protocol commitment since 1997 [47], are well known by public opinion. As easily expected, less consensus was recorded for traditional cultural practices that were surprisingly considered as the same level as organic agriculture. The proposal of discipline for CSFs had medium interest, indicating that other specific actions are actually considered more effective. As expected, the WTP-L class, together with young people, showed distrust in most of the practices.

Respondents considered a guarantee of environment-friendly agriculture to be of use for buying CSFs, particularly for HH, which always appeared more involved in environmental care, while WTP-L, as expected, was shown to pay more attention to cost when buying CSFs. As already shown by Canavari [48], for carbon footprint labels on food, high price sensitivity was coupled with a low WTP for a labelled product. Indeed, this tendency was confirmed by the importance given to a specific label for CSF. WTP-HH, M and H supported a specific label while WTP-L was the least interested since it would increase the cost.

Thus, the classes created for assessing consumer inclination towards CSF on the bases of WTP were helpful for segmenting the population analyzed and provided interesting data. Overall, we found an increased sensitivity in environmental issues from lower to higher WTP-declaring consumers; the first were more interested in economic issues and were more “conservative” while the second ones were more concerned with CC risks with a higher interest in environmental and ethical values (fair trade). In particular, the group with the lowest WTP was the least informed on CC risks and showed the lowest interest in environment-friendly products and social drivers, giving more importance to the cost of the products. The L-WTP class was also less convinced about the capacity of scientists and policymakers in counteracting CC impact. WTP-HH consumers seem to be the primary target for CSFs since they were shown to be informed, to trust science and policymakers in the fight against CC and, regardless of the price to pay, to be more sensitive to a CSF label.

The main novelty of this research was analyzing the inclination of the Italian population towards climate-smart food according to the willingness to pay extra for those products, thus giving insight on consumer behavior. This may help with identifying a target consumer profile and the main drivers that guide consumers in their choices about environment-friendly products.

Since this work was only focused on the Italian population, a possible limitation could lie in the restricted sample analyzed compared to the extent of the CC issue. CSF could be a valuable action to cope with CC; thus, a desirable outcome would be an extension of this study to a wider population in order to successfully introduce CSF on the global market. Moreover, this study analyzed CSFs as a wide category; further studies are then needed to deeply investigate the correlation between WTP and specific food products.

6. Conclusions

Citizens’ choices are recognized drivers in the food market and therefore in agricultural production strategies. Italian consumers may represent a reference model of traditional and cultural heritage approaches to food. This study shows that they have a clear perception of climate change signals and have been showing increasing attention to climate-related food innovation. Indeed, the results of this analysis highlight a positive purchasing attitude towards CSFs for over one third of the population analyzed, also declaring WTP a price premium for a guarantee of environment-friendly food. Consumer-perceived environmental value of CSFs has a significant positive influence on purchasing behavior and thus on the success of the stakeholder’s sustainability-directed marketing strategies.

Such indication may greatly reinforce the adoption of specific actions and the promotion of ad hoc policies to face climate change [49,50], such as as the application of climate-smart agriculture principles (adaptation, mitigation and sustainability).

These findings can be easily extended to other EU countries in order to adopt a climate-smart agriculture approach.

More detailed information on the environment and health would add value to agricultural products, becoming a profitable implementation in information campaigns and public education activities. Moreover, a logo or symbol approach has the potential to be able to drive consumers’ attention to CSF products. An integrated value-chain approach from the production field up to the market will be a win–win strategy in response to CC and socio-economic challenges.